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—Einstein
Process mining
Process mining
Overview
Overview
Process mining is an important tool for modern organizations that need to Identify issues within core processes and manage non-trivial operational processes. It's an important bridge between data mining and business process modeling and analysis.
Everything started long time ago 1999 at Eindhoven University of Technology. At that time event data were not easy available and the initial process mining techniques were extremely naive and unusable in practice. In the last years process mining techniques have matured and event data have become available even in zettabytes .
But what are event data?
But what are event data?
Events may take place inside:
- a machine (ATM, baggage handling system)
- an enterprise information system (order creation, order shipment, delivery creation, price change, credit approval)
- a hospital (blood sample analysis, cell separation)
- a social network (exchanging e-mails, twitter posts)
- a transportation system (check in, ticket purchase)
How can we export event data?
How can we export event data?
Getting a detailed look at your process through process mining means your tools need to extract event data.
One way is to export an event log from the system, giving you for example a csv file that the process mining tool can import. That’s fine, but the most advanced process mining efforts use real-time data ingestion, which is constantly syncing the latest process data.
What kinds of information is contained in these event logs?
What kinds of information is contained in these event logs?
At least some id and timestamp. For example:
ID: a unique reference identifying each business object
level/point: the stage of the process that the case has just gone through
timestamp: the time the case went through that stage in the process
Many event logs have more details than just these three key of information. Perhaps there are details about the vendor, or if it’s a service ticket it might include a priority level.
Process mining techniques use event data to:
discover processes
check compliance
analyze bottlenecks
compare process variants
suggest improvements
Bridging the Gap Between Process Science and Data Science
Bridging the Gap Between Process Science and Data Science
Data science can be viewed as an combination of classical disciplines like statistics, data mining, databases and distributed systems
According some experts process mining is considered as one of the essential ingredients of data science. Unfortunately, the process perspective is absent in many Big Data initiatives and data science projects.
Event data should be used to improve end-to-end processes. It can be seen as a bridge between data science and process science.
Data science approaches tend to be process agnostic whereas process science approaches tend to be model-driven without considering the “evidence” hidden in the data.
The umbrella term “process science” is used to refer to the broader discipline that combines knowledge from information technology and knowledge from management sciences to improve and run operational processes.
Source:
Source
KPIs
KPIs
The goal of process mining is to answer questions about operational processes. For example:
• What happened in the past?
• Why did it happen?
• What is going to happen in the future?
• Why do organizations and people change?
• How to redesign a process to improve its performance?
In order to be able to answer on those questions and to know whether a process mining efforts are successful, we need to define Key Performance Indicators (KPIs).
You can identify three classes of KPIs:
KPIs related to time (e.g., lead time, service time, waiting time, and synchronization time)
KPIs related to costs
KPIs related to quality
Note that quality may refer to compliance, customer satisfaction, number of defects, etc. To evaluate suggested improvements,the effectiveness and efficiency of the as-is and to-be processes need to be quantified in terms of KPIs.
For Lasagna processes, process mining can result in one or more of the following improvement actions:
• Redesign. Insights obtained using process mining can trigger changes to the process,e.g., sequential activities no longer need to be executed in a fixed order,checks may be skipped for easy cases, decisions can be delegated if more than 50 cases are queueing, etc. Fraud detected using process mining may result in additional compliance regulations, e.g., introducing the 4-eyes principle for critical activities.
• Adjust. Similarly, process mining can result in (temporary) adjustments. For example,insights obtained using process mining can be used to temporarily allocate more resources to the process and to lower the threshold for delegation.
• Intervene. Process mining may also reveal problems related to particular cases or resources. This may trigger interventions such as aborting a case that has been queuing for more than 3 months or disciplinary measures for a worker that repeatedly violated compliance regulations.
• Support. Process mining can be used for operational support, e.g., based on historic information a process mining tool can predict the remaining flow time or recommend the action with the lowest expected costs.
How process mining can complement Lean Six Sigma?
How process mining can complement Lean Six Sigma?
Lean Six Sigma is a methodology that combines ideas from lean manufacturing and Six Sigma. Lean principles originate from the Japanese manufacturing industry. The main objectives of the lean manufacturing approach is to improve performance by systematically removing waste. On the other side, Six Sigma focuses on improving the quality of value added activities.
Typically, seven types of waste are mentioned:
Transportation waste: Each time a product is moved increases the risk to be damaged or lost
Inventory waste: Inventory(raw materials) that is not being actively processed can be considered as waste because it consumes capital and space
Motion waste: Unnecessary activities (transformation and double work) result in additional degradation of resources (equipment and people) and increase the risk of incidents
Unnecessary waiting: Whenever goods are not in transport or being processed, they are waiting
Over-processing waste: All additional efforts done for a product not directly required by the customer are considered as waste
Overproduction waste: Producing more than what is required by the customers at a particular time is a potential form of waste
Defects: Rework, missing parts, poor work instructions are defects that can increase the costs of a product drastically
Although this describes more production processes and physical products, the same principles can be used for information/financial services and other BPM like processes.
Many think that Lean Six Sigma methodology and trainings are just a management trend. Process mining can be used as a tool to add more substance to this methodology. For example, process discovery can be used to eliminate all non-value added activities and reduce waste. Unnecessary waiting and rework can be visualized, when the relevant events are recorded.
Conformance checking can also improve the quality of value added activities. Deviations can be found and diagnosed easily, provided that the event data and normative process models are present.
What are process models used for?
What are process models used for?
• insight: while making a model, the modeler is triggered to view the process from various angles;
• discussion: the stakeholders use models to structure discussions
• documentation: processes are documented for instructing people or certification purposes (cf. ISO 9000 quality management);
• verification: process models are analyzed to find errors in systems or procedures (e.g., potential deadlocks);
• performance analysis: techniques like simulation can be used to understand the factors influencing response times, service levels
• animation: models enable end users to “play out” different scenarios and thus provide feedback to the designer;
• specification: models can be used to describe a PAIS before it is implemented and can hence serve as a “contract” between the developer and the end user/management
• configuration: models can be used to configure a system
Business Activity Monitoring (BAM) refers to the real-time monitoring of business processes. Corporate Performance Management (CPM) is another buzzword for measuring the performance of a process or organization. Typically, CPM focuses on financial aspects.
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