Advanced supply chain analytics

Readers have asked us what is “advanced analysis?” If you answer this question logically, you should also define “non-advanced analytics.”

When you evaluate analytics solutions, you need to understand whether the solution is descriptive, predictive, or prescriptive. What are you saying?

Unsurprisingly, you don’t see many vendors talking about their great “non-advanced” analytics solution or managers offering a “non-advanced” analysis project to the CEO.

This discussion highlights that although the term analytic is widely used, it is very poorly defined. And, a poorly defined word with such great connotations risks becoming a buzzword – salespeople call everything they do “analytical” and managers put the word “analytical” in all of their projects.

So before we get into “advanced” analytics, we need to define analytics. If we go back to the Davenport article “Competing on Analytics” in the Harvard Business Review that started the analytics movement, he defines analytics as “the ability to collect, analyze and act upon. Datas”.

In other words, at a high level, analytics is the ability to use data to make better decisions.

Unfortunately, that doesn’t help us much. Haven’t companies always tried to use data to make decisions? – Yes they have. Aren’t there thousands of ways to analyze data? Yes there is.

No wonder people are confused.

Fortunately, academic and professional organizations have understood that the field of analytics should be divided into three categories:

1. Descriptive analysis– using historical data to describe the business. This is usually associated with Business Intelligence (BI) or visibility systems. In the supply chain, you use descriptive analytics to better understand your historical demand patterns, to understand how products flow through your supply chain, and to understand when a shipment may be late.

2. Predictive analytics– using data to predict trends and patterns. This is usually associated with statistics. In the supply chain, you use predictive analytics to forecast future demand or to forecast the price of fuel.

3. Prescriptive analysis– using the data to suggest the optimal solution. This is usually associated with optimization. In the supply chain, you use prescriptive analytics to define your inventory levels, plan your factories, or route your trucks.

Having this definition gives you a better framework for evaluating analytics projects and understanding how they can help your supply chain. Note that this does not suggest that one type of scan is better than another – different problems require different solutions.

Once we have this definition, we no longer need the generic term “Advanced Analysis”. For various reasons, BI systems and some statistical solutions have become synonymous with the term analytics. So, to differentiate themselves, vendors offering optimization solutions, new complex statistical methods, or something that they thought was a breakthrough tried to label their solution as “Advanced Analytics”. Of course, once some vendors start using the term, others will follow suit.

Final thoughts

When you evaluate analytics solutions, you need to understand whether the solution is descriptive, predictive, or prescriptive. Then, within each of these categories, you can determine whether the solution is more basic or more advanced and what will meet your needs.