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RUCHI SHAH: Hi, my name is Ruchi Shah,
I'm an enterprise sales engineer at UiPath.
And this is a demo on using process mining to analyze
in Order-to-Cash process.
Today, we'll be playing the role of an analyst
in the financial management team.
As an analyst focused on the Order-to-Cash process,
it's important for me to assess process deviations
and compliance issues, and help manage these risks.
Using process mining I'm able to perform algorithmic process
comparison, analysis, and validation while monitoring
and managing business activity.
In many organizations, businesses have internally
defined standard operating procedures that define
how the process should be done.
UiPath process mining helps validate or audit
whether actual operations are in conformance
with these defined policies without having to crowdsource
with every employee.
By ingesting up to a billion rows of data per application,
process mining can extract, transform, and load data
directly from source systems, and visualize
in our app for our compliance teams to review and monitor.
Upon opening my Order-to-Cash application,
I'm welcomed by a process graph with surrounding
KPIs and charts that provide supporting data
for an initial overview of my process.
As an auditor, specific frames are extremely important to me,
and the ability to have flexibility
to change time periods quickly and easily
help me speed up time to analysis.
Once I've set the time period, I'm most interested in viewing,
I can now visualize the entire end-to-end process
in a visual model or process graph.
Each container represents the activity
or the step that occurred in the process,
as well as its connecting edges.
The darker the edge or activity, the more frequent the path.
If I want to trace how most expensive SO items traverse,
I can always change the scope of my process graph
to represent the average item value.
The process graph itself is also interactive.
If I see an abnormal step in the process,
I can filter for that step directly from the process
graph, and investigate why this anomaly occurs.
For example, a large number of sales order items
get their order quantity changed.
Any type of changes made to the sales orders
could add on top of each other potentially delaying orders.
Additionally, we could simply filter for late deliveries.
A major flag in cost expense for our business,
and it needs to be investigated at once.
Filtering for this edge directly from the process graph
allows me to analyze further the cases that have this anomaly.
Now that I have late deliveries filtered,
I can use the new Root Cause Analysis dashboard,
and can uncover trends within my data that significantly
influence this path.
As I extend my horizontally expanding tree,
it's clear to me which nodes are most
impacting these violating cases as well as nodes that
do not violate.
Additionally, based on how frequently these fields impact
my problem filtered set, I can uncover significant influencers
within my data.
Not only do these significant influencers
have a high percentage of impact,
but they also occur many times within my process
driving me to focus on these initial areas.
I see that sold for resale distribution channel
has a high influence for these late deliveries.
And the material group, mobile and pharmaceuticals,
being most impacting this distribution channel.
Double clicking into one more level
I can see that more specifically these
are the materials most impacting this distribution channel
and its late deliveries.
I can continue to drill down into these high influencing
nodes expanding my tree and analyzing as many dimensions
as my data necessary in order to uncover the true root
cause of these violating areas.
With these process gaps and inefficiencies uncovered,
I can now focus my energy on improving
this influential path.
With root cause analysis, now that I
have a better understanding of the customer and the people
that significantly influence the anomaly I initially
found in the process graph, I can
use my powerful and flexible Filter panel
to quickly and easily build filters
on top of each other to analyze a specific set of cases
to better visualize their actions in the process graph.
Knowing sold for resale is a distribution channel struggling
to meet standard operating procedures,
I want to understand the difference
between more performative channels,
and see how they compare.
My Filter panel includes Select All, Deselect All, and Inverse
Capabilities, as well as the ability
to search for all the fields in my data set in order to build
an enriched set of filters.
With the compare mode and process mining,
the original filters set will automatically
be set as scenario A, and the inverse defined as scenario
B. Inside my filter panel I can continue to add or adjust
any number of fields to filter on as I define scenario A
and B. Scenario A is represented with an orange color,
and scenario B with a pink.
Not only can I see scenario A and B on my process graph,
but it's reflected on all charts and components
within all my dashboards.
Additionally, all the calculated metrics
are shown both for scenario A and B,
and I can visualize and compare the value
of these items, their throughput time, and the impact
each scenario has on the business, especially
the non-conforming ones.
Comparing these metrics allows me
to understand how much time and money
I could potentially save by improving my process.
It's clear to me now that an additional reason why
sold for resale is so unsuccessful
is because there are so many additional change
actions happening for this distribution channel.
These additional steps are, of course,
going to add on time and manual effort,
and lengthen my delivery cycle.
To better improve my resale distribution channel,
I can send this idea to automation hub
as a recommendation for a process improvement opportunity
to better increase efficiency for the team.
I can continue to visualize where scenario A deviates
from scenario B the time differences, the KPI
differences.
And if I wanted to understand the supporting data
and their variations, I could additionally
toggle my dashboards to learn more.
With the standard operating procedure defined,
I can build a combination of directly follows sets
to filter for cases that adhere to what I'm expecting,
to in turn build a model for additional analysis.
In other words, I can create on the fly
my own path whether it be based on a standard model,
or I can evaluate a brand new model that may be even more
beneficial for the business.
Creating this path allows me to define and analyze
new operating procedures based on historical events.
It looks like these two variants are following the protocol,
and all the other variants are not.
Let's continue filtering for these variants,
and use a Compare capability to investigate
the conforming variants versus the non-conforming variants.
We can see that the main differences in scenario
B, the non-conforming variants, is that there are again,
more change steps taken.
Looks like there could be some process improvement
items that we could work on to improve these types of cases,
and better train our employees and improve relationships
with our customers, companies, or suppliers,
preferably tackling those use cases with larger
numbers of items.
Or perhaps our way of doing business has expanded,
and our standard operating procedure
needs to be better defined.
Again, any recommendation for process improvement
or automation potential can be submitted directly
to automation hub.
A centralized repository not only for automation, but also
for process improvement as well.
Investigation into these additional steps and process
deviations can be documented using assisted text mining
to get a clear understanding of how employees
are performing these processes.
On a case level view, I can drill
into how each case is performed from beginning
to end, and get a clearer understanding of how
these problematic cases are traversing
on an individual level.
By investigating each case, I gain another perspective
into why these cases are going wrong.
As my team continues to identify opportunities for improvement
within the Order-to-Cash process,
I can add important business metrics or KPIs
in process mining to continuously monitor and track
how my changes are impacting my process.
In the Summary page, new business initiatives
have come into place, and I'll need
to add more KPIs such as variance
to monitor how my process improvements have impacted
the different ways users perform the process.
As analysts enter the app, they'll
have an understanding of the KPIs
that are important to them, and visualize
how they're trending over time as we upload new data.
In my previous analysis, I also noticed
tension between customer and purchase order creators.
I can build my team a dashboard specifically
to monitor these interactions, and customize and design
my dashboards layouts, charts, and KPIs all with a no code
experience.
This can act as a page my audit team monitors
on a daily or a weekly basis to ensure
that our efforts in retraining process
improvement and automation can help make the process more
compliant.
Every dashboard has KPI headers that we can add.
I'll add some relevant KPIs that are specific and relevant
to my audit team.
Next, I'll create a pie chart based on purchase order
creators, and their average number of events.
Ideally, we bring these down and for all of our sales
orders to save the team some time and effort.
Next, I'll create a table to have
a nice summary of the cases that are occurring,
and the properties that I'm interested in such as material
and distribution channel.
And lastly, I'll build a line chart
to continuously monitor the number of actions or events
that happen.
As I improve my process, I hope to see this line trend
downwards over time.
Now that I've finalized my dashboard
for continuous monitoring for my team,
I can publish those changes.
With process mining, you've seen our process graph
in action, the flexible and extensive
filtering capabilities, root cause analysis, tags,
case details, and app editing.
All capabilities to help drive process compliance, modeling,
monitoring, advanced process analysis, recommendations, as
well as ways to track process improvements.
To learn more about process mining,
visit uipath.com/process-mining.
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