Process Mining Basics
Process Mining? Data Mining? Business Intelligence? There are many analytical terms and it can be challenging to understand them all. This course will help you understand the terms associated with Process Mining and how it can be used to improve business processes.
Before we dive into the world of Process Analytics, let's first understand:
- What is Process Mining?
- What are key terms in Process Mining?
- What are the benefits of Process Mining?
- Why do companies use Process Mining analytics?
Let's start with the basics.
Combining Analytical Fields
Process Mining combines different analytical fields to analyze operational processes based on their actual "as-is" data, which is available as log files in ERP-Systems. It's derived from the field of Data Mining as it uses similar technology to mine processes for improvements.
The goal of Process Mining is to transform data into actionable insights by revealing the current state of processes and create opportunities for improvement.
The Focus - Business Processes
In comparison to Data Mining, Process Mining focuses on business processes and its transactional data in ERP systems.
The idea of Process Mining is to discover, monitor, and improve business processes.
Throughout processes and operations, every detail gets recorded. These details leave behind a trace called digital footprints and can be found throughout different systems in an organization. Process Mining captures these footprints and allows you to visualize them in a step-by-step journey view.
The most system-based tasks get recorded in log files, so-called event logs.
Process mining uses event logs and process-related data to "mine" processes and better understand them. The full understanding of processes includes:
- Identifying process trends, patterns, and deviations
- Detailed visualization of actual processes
- Discovering new ways to increase process efficiency
All executed steps are revealed and can be analyzed for conformance and inefficiencies (such as rework or redundant tasks).
Process Mining uses techniques from Business Intelligence to visualize this information and hence provide valuable insights
Based on the findings, the respective process improvements can be defined, for example:
- Implementing approval steps to ensure compliance
- Removing redundant tasks to reduce cycle time
- Change process flows for efficient execution
Let's learn about the terms used in Process Mining.
When we talk about Process Mining, it's important to clarify the common terms. Let's use an example to explain the key terms. Imagine you work at a company selling T-shirts with custom prints to customers (customers can also buy ones with existing print). The following terms are commonly used:
Process Model Diagram
Most companies already have existing process models, which visually describe the sequence of tasks and responsibilities ("to-be" process). The process model below, visualizes all necessary tasks "to be" executed in order to complete the process.
A Case defines a one-time execution of the process (complete or incomplete). The blue dots represent the different department tasks. The red dot represents the tasks that have been executed in the process.
A variant is a set of cases with the same process flow (same choices, activities, events).
Only small differences (different end-decision) will lead to a new variant. The example below shows 3 different process variants (based on 4 cases).
In Process Mining, events define every action within a system and these actions have a specific timestamp. Assuming the process is fully executed, each individual task is considered an event since we receive a system-generated timestamp on each task.
For example, when the shirt is sent for printing, this is considered an event and has a time-stamp associated with this specific task.
Besides the specific tasks and time-stamps, there is other information related to the process called Attributes.
Task attributes provide additional details on the executed tasks for certain cases, such as cost center, type of execution (user or system), and costs.
Case attributes provide additional details on cases, such as order type, method of payment, and choice of shipping speed. See example below for case attributes.
The Core of Process Mining
What are the capabilities of process mining?
Process Mining is Irresistible
Capabilities of Process Mining
So far, we've learned that Process Mining captures digital footprints of system-based tasks to deliver insights into the actual operational process. Sounds great! But what exactly are these insights?
Process Mining has 3 important capabilities:
Process Discovery visualizes the way executed tasks are performed and backtracks the process flow.
This helps companies to:
- Identify the actual "as-is" process in a system
- Identify process deviations, exceptions, or outliers
- Identify the critical path (the longest sequence of tasks from start to finish) and the total number of executions
- Get a starting point to dig deeper into processes
Process Conformance focuses on the mapping of "as-is" data to an existing predefined "to-be" process, e.g. a BPMN process model.
This helps companies to:
- Identify the total of non-conform cases
- Identify the root course of each case
- Develop measures to ensure conformance
Process Performance is about measuring the process and define performance indicators that are shown in a dashboard.
This helps companies to:
- Create and evaluate measures for improvements
- Benchmark performance (e.g. of different regions)
- Monitor the overall performance process in real-time to react more quickly in case of changes
Why do Companies Use Process Mining?
There are several reasons why companies investigate and improve their operational processes, but the main one is a common reason that impacts almost every business.
Digital Transformation is one of the most used terms in today's world and will be a challenge for many companies in the next years.
Digital Transformation means the change in process of a company to adapt to the fast changing global market and customer expectations by using digital technologies and implementing a digital infrastructure. It combines the latest innovative tools and processes with the company's expertise in order to meet customer expectations and ensure a competitive position for the business.
This means that many companies need to adapt their processes in a way which new technologies can be implemented to meet customer expectations and gain new customers. Previous analog processes need to be transformed into digital ones.
Digital transformation combines the latest innovative tools and processes with the company's expertise in order to meet customer expectations and ensure a competitive position for the business.
Quote from Jeff Bezos
Three Important Topics
Big Data and Real-Time Process Analytics
Data mining helps identify trends, patterns, and customer behavior in a large data set. Combined with Process Mining, this allows a seamless stream of operational process and production data for real-time analytics.
Cloud technologies allow for remote resources, storage and process data on demand, and the reduction of hardware and infrastructure costs. Cloud-based Process Mining tools provide powerful connectors to many systems and analyze millions of cases within seconds to deliver process insights.
Artificial Intelligence (AI) and Machine Learning (ML)
AI gives companies a completely new basis for decision-making, enabling them to make more informed decisions. Due to its rapid development, it's already implemented in many industries, including IT, financial services, automobile manufacturing and healthcare. AI in Process Mining, is based on actual "as-is" data and can predict the process for cases, for example, incoming orders.
Alright! Now that you've learned the role of Process Mining with digital transformation, let's now look at the impact and benefits of Process Mining.