Uncover hidden connections and causalities in process data & investigate where and why problems occur.
Running business operations is a challenging task. It’s not just about spotting issues and faults. You need to become a detective and look for true causes of these problems.
We are introducing a feature based on Machine Learning to act as both your magnifying glass and a guide in finding the right answers.
By tracing back information from process data, AI-Powered Root-Cause Analysis allows you to investigate what happened and why, helping you decrease the chances of letting the problem happen again.
When you bump into an anomaly in your dataset during a process analysis -- for example, the Order-to-Cash (OTC) process is taking too long -- you know it’s just the beginning of your work. You need to dig deeper and learn what is behind, why it happens, which specific attributes (suppliers, plants, materials, etc.) are causing it.
In other words, your goal is to identify the combination of attributes having the most negative impact on the problem.
Let’s say you would like to get to the root cause of the lengthy OTC process where the average case duration is 46 hours. The dataset also provides information about case level attributes such as supplier country, supplier city, material, total amount, and cost center.
How can you find out which of these attributes, or a combination of them, causes the process to take so long?
Pro Tip: Predefined Dashboards templates, added Process Compare Map Widget, Project Export and Import – see what’s new in Minit 5.2.
To get the answers, run the AI-Powered Root-Cause Analysis, which is based on intelligently selected Machine Learning algorithms.
With the Root Cause Analysis, you will be able to compute the combination of the influencing attributes and the root causes of faults and problems your process experiences. It helps you uncover hidden relations between attributes.
By looking at each value of each OTC process attribute separately, you'll see that the highest influencer of the OTC process duration is when the supplier city is Graz. On average, it increases the duration of the case by an additional 15 hours. This initial analysis also tells you that the other values of attributes influence the target metric far less.
However, when you start splitting the Root-Cause Analysis tree, you’ll notice that the Graz plant is performing poorly only when the material attribute is aluminum, while performing above average with every other material.
The steps to run a Root-Cause Analysis are simple:
- Select any metric that you want to explore (even custom-created)
- Select some case level attributes which you think can influence the metric
- Start analyzing and continue splitting the dataset to get to the root cause
- If you feel the default split is not right, you can adjust it as you see fit
The AI algorithm computes a tree-like structure where each node makes the best statistical split of the dataset into two smaller parts, based on correlations.
For better visualization, horizontal and vertical layouts of the tree are supported. As the root-cause tree can get quite large, a diagram preview will allow you to zoom in and out, so you don't get lost.
Root-Cause Analysis, following a process discovery phase, helps you investigate operational issues on many levels objectively, based on hard data. It also allows you to convert all the findings into filters for further analysis of particular cases.
From now on, you will be able to quickly answer such questions as:
- Why some cases are slower than others?
- Why some cases get stuck in rework?
- Why some cases have more waiting time?, and many more.
Now you’ll know which plants are triggering the longer cases duration, which suppliers are causing delays, why some decisions are stuck in rework. Such an analysis will later create the basis for your process improvement initiatives.
Pro Tip: Once you know where the issue occurs, create Business Rules to stay alerted if the KPIs and standards are met. More about Business Rules
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Juha Byman, Head of Advanced Analytics