|This content applies solely to Process Mining, which must be purchased separately from the Appian base platform.|
Estimated time to complete tutorial: about 1 hour
User experience level: Beginner
In this tutorial, you'll use Appian Process Mining to discover insights and optimize an order-to-cash (O2C) process. This process you'll optimize is for a pizza restaurant named Process Dining Pizzeria that's located in New York City. You'll use Mining Prep to transform and load process data from their order processing system. You'll use Process Mining to discover how your process actually runs, and you'll perform root cause analysis on process deviations to identify optimization opportunities.
For this tutorial, you'll be assuming the role of an analyst for Process Dining Pizzeria with the goal of improving the efficiency of their ordering system. Customer service has received complaints about orders taking too long and pizzas arriving un-sliced. Let's see how we can resolve these problems using Appian Process Mining.
This tutorial is designed to be used with:
This tutorial will go more smoothly if you understand these terms, used in the context of Appian Process Mining:
Before you begin, make sure to complete the following steps.
To complete this tutorial, you'll need to download and unzip the following file: Process-Mining-Tutorial.zip. This ZIP file contains:
Download and unzip
Process-Mining-Tutorial.zip file before you begin the tutorial.
The administrator of your Appian environment must set up the following to support your work in this tutorial:
|Process Mining||Create a user account for you to use. This account needs Analyst access.|
|Mining Prep||Connect your Mining Prep and Process Mining environments with the shared API key.|
Where? Mining Prep
The first step in any Appian Process Mining project is to import data that describes your process. Typically, you need to transform the process data into a format that Process Mining can read. This format is called an event log.
In this part of the tutorial, you'll use CSV files from Process Dining Pizzeria's order processing system in Mining Prep to:
Let's get started!
To begin, you need to sign into your Mining Prep environment.
To sign in to Mining Prep:
It's often the case that your process data is split into a few different files or data sources. In this tutorial, Process Dining Pizzeria's data is in three files: a table of events that occurred as part of the order process, the associated case attributes, and a key that maps location IDs to location names.
You need to add three data sets for each of your CSV files.
pizza-order-events.csv as a data set:
pizza-order-events.csvfile you downloaded earlier.
The data from
pizza-order.csv displays in a data set as shown below:
case-attributes.csv as a data set:
case-attributes.csvfile you downloaded earlier.
location-key.csv as a data set:
location-key.csvfile you downloaded earlier.
Now that you've imported the CSV files into data sets, it's time to add the data sets to a transformation project. Here, you'll transform and combine the data into an event log for Process Mining.
Because Process Dining Pizzeria has a case attribute file and an event file, we'll keep those in separate building blocks. For large data sets, this can help keep your data organized.
To create a transformation project:
Pizza Order Process.
The transformation project is empty, so you need to add one of the data sets you just uploaded. Let's start with pizza-order-events.
To add a data source:
The data will populate into the building block.
To transform your data into an event log, you add a sequence of transformation actions that will execute when you load the project into Process Mining. At a minimum, your data needs to have a case ID, event, and time stamp.
In this example, we have time stamps, but they are currently assumed to be of type
string as denoted by . Let's convert the time stamp columns into a proper
To convert dates:
Notice that the data type icons change from string to datetime for the start and end columns.
That's all you need to do for the pizza-order-events building block for now. Next, let's make a new building block and add the case attributes to it.
Case attributes are characteristics of an entire case such as order cost and order type. It's typical to use multiple building blocks in a transformation project when you have a large amount of data and when event and case data are located in separate files. Building blocks help you organize data in Mining Prep.
To add a new building block:
Now we have a blank building block. This should resemble the blank building block you originally added pizza order events to. Let's add the case attribute data here.
To add case attribute data:
One of the most commonly used transformation actions in Mining Prep is Add Data. This allows you to combine multiple data sets into a single building block.
In the current data set, only numeric keys represent locations. Fortunately, the
location-key.csv file maps these numbers to a location name.
To add location data to this building block:
You've successfully combined all of the case attribute data into a single building block! Let's perform a couple more actions to clean up the data.
Next, notice that there are Location and LocationID columns. Since the data only includes boroughs in New York City that Process Dining Pizzeria delivers to, we can make this more specific. Let's rename these columns to Borough and BoroughID respectively, so that the column more accurately represents your data.
To rename the Location column:
To rename the LocationID column:
The merge transformation action combines data from two columns together. This is helpful when you have related data that you'd like to group and view together in Process Mining.
To merge columns:
-and add a space before and after the hyphen.
The Bronx - 1.
Once your data set has been transformed, you're ready to identify the required data properties. In order for data to be analyzed by Process Mining, it must contain a case ID, event, time stamp, and optionally, case attributes.
Since our transformation project has two building blocks, we'll have to identify the case ID in both places.
The case ID ties together an entire sequence of events, comprising a unique case or process instance.
In this example, you can use the order number as the case ID, since it represents a unique case or process instance.
To identify the case ID for case-attributes:
Case attributes are characteristics of an entire case. When you identify these attributes in Mining Prep, Process Mining makes them available as dimensions in dashboards to help with your analysis. In this tutorial, Process Dining Pizzeria's
case-attributes.csv file contains all of the case attributes.
To identify case attributes:
Why didn't we select these attributes when identifying the event? All of these attributes are case attributes, not event attributes. Case attributes apply to an entire case and don't change from event to event. Event attributes apply separately to each event.
We also need to identify the case ID for pizza-order-events. When we transform and load the data into Process Mining, the shared case ID will tie the cases together.
To identify the case ID for pizza-order-events:
Events represent how and when a process activity occurred. In this example, these are stored in the pizza-order-events building block.
To identify events:
Nice work! You're now one step away from analyzing this data in Process Mining.
At this point, you've prepared the data to be transformed into a suitable format for Process Mining analysis. All that's left is to transform and load it into Process Mining.
To transform and load the project:
You did it! Now it's time to start digging deeper into this data and uncovering actionable insights.
Where? Process Mining
The process data is now ready for Process Mining analysis.
In this part of the tutorial, you'll use Process Mining's capabilities to:
To sign in to Process Mining:
After you sign in, you should see the Pizza Order Process event log in the management hub. Most actions you take in Process Mining are done through the context of the log you select. Notice that the sidebar is empty; this is because you need to select the Pizza Order Process event log to proceed.
To select the event log that describes your process:
Notice that the Pizza Order Process event log is now active within Process Mining. Models, deviations, filters, and insights are now all viewed through the context of this process.
When you select an event log, you're immediately navigated to the Models page. Here, you can see a visualization of your process as described by the event log.
While it's useful to visualize how your process actually executes, it's even more useful to compare the actual execution with how you expect it to execute.
The discovered model tells you how your process is actually occurring, but how do you uncover insights into specific behavior? A great approach is to upload a target model, which is a BPMN file that describes how you expect your process to run. Process Mining performs conformance checking against this model to tell you when certain activities are skipped, out of order, or require additional work.
Let's upload the target model BPMN file provided in Step 1.
To upload a target model:
When you view the target model, you'll see how your actual process compares. For more information, see target models.
Variant groups are collections of variants based on the frequency they occur. Variants are alternate paths that a process can take. Slide the Number of Variant Groups slider to reveal more process variants. Let's show all variants.
To show all variants:
Notice that additional deviations are uncovered in the target model. Notably, there are several variants where the Slice It activity is skipped.
Let's see what other deviations exist in our data on the Deviations page.
Deviations are changes to an activity that negatively impacts a process's normal execution. These are often good candidates on which to perform root cause analysis. Root cause analysis helps you determine why a deviation is occurring, so you can take action to eliminate it.
To identify deviations:
Notice that there are three different types of deviations in the process. For more information on the possible types of deviations, see Deviation types.
Let's use the Activity skipped deviation that we just looked at to perform root cause analysis.
Root cause analysis helps you determine why a deviation is occurring, so you can take action to eliminate it. In our use case, we want to see why the Slice It activity is getting skipped in 59 of the cases.
To perform root-cause analysis on a deviation:
You are navigated to the Insights page, which displays the results of root cause analysis. Let's walk through this page to uncover insights.
The Insights page displays the results of root cause analysis.
At the top of the page, you'll notice that the Slice It activity is skipped in 5.9% of the cases in the log. This is a significant enough problem that customer support has been notified that pizza was unsliced.
The Attribute Comparison Analysis section indicates how likely a specific attribute is impacting the process outcome. The higher the number in the Potential Impact column, the greater the likelihood. Notice that the Borough attribute name has a potential impact of 1.0. This points to a strong likelihood that the location of business is leading to the deviation.
To dig deeper, we can plot the values of the Borough attribute. It's important to distinguish between the attribute values to see if any stand out as the culprit.
Click PLOT in the Borough attribute row to display a graph of how the Borough attribute value is distributed throughout your data.
Notice that the Manhattan location contains all of the affected cases! This is an important data point, which is further backed up by the Causal Rules for Affected Cases section. In this other section we see that when Rule
Borough = Manhattan, there is 100% coverage of affected cases.
What could this mean? There is a strong chance that the restaurant in this location is correlated with pizzas going out without being sliced. This could be a number of factors, such as inadequate training or the restaurant being understaffed and overworked.
You've already looked into why pizzas are arriving unsliced, but you still need to look into orders taking too long. Let's create a dashboard to look into the wait time between Receive Order and Bake activities. Here, you can use the Borough_with_ID categorical attribute you defined earlier to see if there is any correlation.
To create a dashboard:
Wait Time by Borough.
Dashboards give you a high-level view of metrics and dimensions in your data. In the dashboard you just created, it's clear that the Queens borough stands out from the rest with its longer wait times from Receive Order to Bake activities.
The great part of a dashboard is you can now monitor how this metric improves over time. For example, let's say you make changes to the process to better automate how employees receive and accept orders. When you upload new data to Process Mining, you'll be able to track how that affected the metrics you are tracking.
Now that you've uncovered insights into what is causing process deviations, it's time to implement an improvement to your process. Fortunately, Appian is well-suited to creating, enhancing, and automating processes.
To improve this process, you could:
Process mining is a cyclic improvement cycle. After you try techniques to improve your process based on the initial goals you defined, you can schedule automatic transform and load updates to track your progress towards goals. You can also use additional Process Mining capabilities, like dashboards and process scorecards, to track and share summaries of process performance.
Congratulations on finishing this tutorial!
For additional information about other Process Mining use cases, see Use Cases.
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