SUPPLY CHAIN ANALYTICS – TYPES OF SUPPLY CHAIN ANALYTICS:

Supply chain analytics is not a single tool that helps you build a better supply chain. Instead, supply chain analytics is an entire toolbox filled with different kinds of tools.

Hammers, screwdrivers, saws and pliers each does something different to help you build a shelf. A supply chain analytics toolbox also has different types of tools.

Analytics tools. Each helps you understand and improve a supply chain in different ways. Let's begin with the four traditional analytics tools:

Descriptive - Descriptive analytics tell you what happened:

Let's look at some simple examples. Here are some descriptive analytics. Last week, 10% of our shipments arrived late. And customer complaints were up 18%. These analytics tools told me we had a bad week.

Diagnostic - Diagnostic analytics tell you why that happened:

Let's look at diagnostic analytics. Last week, we had 25% more orders than usual. We had three trucks that required maintenance, and one of our drivers was on vacation. These help us understand why we had a bad week. Now that we understand the past,

Predictive - Predictive analytics tell you what might happen next:

let's look to the future with predictive analytics. Based on increased traffic on our website, our models predict sales will increase in the next 14 days. Also, based on the age of our fleet, we expect that most of our trucks will need maintenance in the next month. These help me see some potential problems ahead. So what should we do?

Prescriptive analytics - Prescriptive analytics tell you what you should do next:

Based on data and advanced calculations, prescriptive analytics might recommend buying new trucks to minimize late shipments and reduce the long-term cost of the fleet. In addition, the models recommend increasing order sizes. Those are the four traditional analytics tools.

Based on data and advanced calculations, prescriptive analytics might recommend buying new trucks to minimize late shipments and reduce the long-term cost of the fleet. In addition, the models recommend increasing order sizes.

Each gives you different information.

Those are the four traditional analytics tools. Some companies develop their own analytics tools. Others might rely on pre-programmed software suites developed by consultants, which brings us to even more complex analytics tools. Supply chain optimization simulations, and cognitive analytics.

Supply chain optimization software looks at one small part of an existing supply chain and tries to find the perfect combination of decisions that will theoretically optimize that process.

Supply chain simulations are also used to map out a new process. By running the simulation, we can potentially identify problems, as well as strengths for the new process before it's built.

Cognitive analytics utilize machine learning to comb through massive amounts of supply chain data in the hopes of learning and deciding quickly.

In life, sometimes you need a hammer and saw. Sometimes you need a bulldozer and a crane. The same thing happens in the world of supply chain analytics. Sometimes basic analytics do the job. Other times, you need more complex analytics tools.