- "Without data, you're just another person
with an opinion."
But that's not enough.
In order to have an impact,
we need to transform that data into insights.
In this video, we'll look at five types of data analytics
that we can use to analyze, optimize,
and automate our supply chains.
Analytics is a journey that always starts
with collecting and cleaning and organizing our data.
Once that's done, we can choose
one of the five different approaches to analytics.
The most basic approach is descriptive analytics.
Descriptive analytics involves sorting and filtering data
so that we can see patterns
and describe what has happened in our supply chain.
When we perform descriptive analytics,
we often use statistics.
We could be looking at past sales records,
production volumes, or inventory levels.
And we might be talking about average inventory levels
or average lead times.
Next is diagnostic analytics.
Diagnostic analytics compares what actually happened
to what we expected to happen.
For example, we could use diagnostic analytics
to compare a sales forecast to our actual sales results.
Diagnostic analytics is useful for doing root cause analysis
and implementing statistical process control.
So it's often combined
with continuous process improvement approaches,
such as Lean and Six Sigma.
Then there's predictive analytics.
With predictive analytics,
we use the past to predict the future
or in more technical terms,
we use historical statistics
to estimate future probabilities.
We can use predictive analytics
to think about how we should prepare for managing risks.
Let's say our warehouse is located in a region
that floods once every five years.
An example of predictive analytics would be
to project that there's a 20% chance of flooding next year.
Many supply chains are now going a step further
and using artificial intelligence
to implement prescriptive analytics.
Prescriptive analytics combine historical data
with real-time inputs to recommend actions.
You may hear this called a recommendation engine,
because the system is actually giving suggestions
to a human about what actions they should take.
A common challenge with prescriptive analytics
is that humans may not understand
why the recommendation was made.
The recommendation engine
can seem like a mysterious black box
that just churns out instructions.
At the highest level, we have advanced analytics,
where computers are able to make decisions autonomously.
That is the long-term goal
for many digital transformation efforts.
All five levels of supply chain analytics
play a role in digital transformation.
By understanding the differences,
you can choose the right approach
for the challenges you're facing today
and for the supply chain you're building for the future.