Process and task mining helps organizations discover and visualize all business processes across the enterprise, and analyze the traces of process related data recorded by IT systems, providing greater business process transparency and optimization.
The analytics tools of process mining helps users to analyze vast amounts of data in real-time, providing end-to-end operational intelligence in real time.
Process experts often design processes expecting that their execution will be faithful to the design. However, designs tend to be incomplete and not executable in practice, and people tend to develop workaround to bypass designed processes or to compensate for the inconveniences. Process mining highlights the deviations and helps designers and managers to modify the designed processes in light of actual working of processes or to train the operating staff in designed processes.
These challenges are more prevalent than ever as rapid changes are occurring and getting introduced in the processes due to digital transformation programs, adoption of global business services, and the introduction of bots and other AI into the business processes.
Process and task mining offer a set of novel tools and techniques for the factual analysis of business processes. Based on system logs and/or screen recordings, they automatically map and visualize how processes have been executed in reality, and help companies to improve processes and standardize to much a greater extent.
Topic of Computer Aided Industrial Engineering (CAIE) - Proposal by Prof. Narayana Rao K.V.S.S.
IISE 2021 Annual Conference Paper.
Process Mining is a new area of study grounded in a long tradition of businesses striving to optimize business outcomes by improving the efficiency, effectiveness and productivity of their critical workflows.
Frederick Taylor Winslow was the first person to study and optimize workplace productivity. His publications, 1895 (Piece rate system), 1903 (shop management) 1911 (The Principles of Scientific Management) pioneered the idea that a business’s core operations should be analyzed, standardized and improved on.
Evolution of Process thinking: Taylor - Ford's Mass Production - Toyota Production System - Six Sigma
Process Mining happens in four distinct stages
• Collection of time-stamped event log data from key transactional systems
• Discovery within that data of real processes taking place
• Enhancement of those processes to increase and optimize efficiency, effectiveness and productivity
• Monitoring these changes for further adjustments required to make them standard operations.
“Process Mining is analyzing processes based on event data... i.e., based on what’s really happening."
For based on the event logs, process discovery and conformance checking is done. Process improvement engineers re-engineer processes.
Process Mining software systems are purpose-built to handle the inherent complexity and dynamism of the modern process environment. It delivers deep visibility and control into the minutiae of individual processes, the relationships between them, and the outcomes they deliver.
Reference
Celonis, The Ultimate Guide to Process Mining: A handbook for process excellence
What is Process Mining?
24 Feb 2022
IBM Technology
Process mining is technique that applies data science to discover, validate and improve workflows by extracting available knowledge from event log systems in an organization.
In this lightboard video, Jamil Spain with IBM, explains how process mining can help a business better understand the performance of their processes, find bottlenecks and other areas the need improvement.
https://www.youtube.com/watch?v=YNxpGimyCt0
Process Mining Manifesto
Process mining is sits between computational intelligence and data mining and process modeling and analysis on the other hand.
The idea of process mining is to discover, monitor and improve real processes (i.e., not assumed processes) by extracting knowledge from event logs readily available in today’s (information) systems
Process mining includes (automated) process discovery (i.e., extracting process models from an event log), conformance checking (i.e., monitoring deviations by comparing model and log), social network/organizational mining, automated construction of simulation models, model extension, model repair, case prediction, and history-based recommendations.
Process mining is an enabling technology for continuous process improvement (CPM), BPI, TQM, Six Sigma, and the like.
Starting point for process mining is an event log. It is possible to sequentially record events that happen in a process such that each event refers to an activity (i.e., a well-defined step in some process) and is related to a particular case (i.e., a process instance). Whenever possible, process mining techniques provide extra information such as the resource (i.e., person or device) executing or initiating the activity, the timestamp of the event, or data elements recorded with the event (e.g., the size of an order).
Event logs of the processes (actual runs) can be used to conduct three types of process mining. The first type of process mining is discovery. A discovery technique takes an event log and produces a model or chart of the process utilized. The second type of process mining is conformance. Here, an existing process model is compared with an event log of the same process. Conformance checking can be used to check if reality, as recorded in the log, conforms to the model. The third type of process mining is enhancement. Here, the idea is to extend or improve an existing process model using information about the actual process recorded in some event log. To support enhancement, by using timestamps in the event log, the process model can show bottlenecks, service levels, throughput times, and frequencies.
Guiding Principles for Design of Process Mining System and Development of Process Maps
GP1: Event Data Should Be Treated as First-Class Citizens
GP2: Log Extraction Should Be Driven by Questions
GP3: Concurrency, Choice and Other Basic Control-Flow Constructs Should be Supported
GP4: Events Should Be Related to Model Elements
GP5: Models Should Be Treated as Purposeful Abstractions of Reality
GP6: Process Mining Should Be a Continuous Process
Challenges
C1: Finding, Merging, and Cleaning Event Data
C2: Dealing with Complex Event Logs Having Diverse Characteristics
C3: Creating Representative Benchmarks
C4: Dealing with Concept Drift
C5: Improving the Representational Bias Used for Process Discovery
C6: Balancing Between Quality Criteria such as Fitness, Simplicity, Precision, and Generalization
C7: Cross-Organizational Mining
C8: Providing Operational Support
C9: Combining Process Mining With Other Types of Analysis
C10: Improving Usability for Non-Experts
C11: Improving Understandability for Non-Experts
Process Mining
Manifesto released by the IEEE Task Force on Process Mining.
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4 Ways Process Mining Uses Automated Root Cause Analysis
UPDATED ON AUGUST 8, 2022 | PUBLISHED ON MARCH 3, 2022
https://research.aimultiple.com/automated-root-cause-analysis/
Ph.D Student Senderovich Arik
Subject Queue Mining: Service Perspectives in Process Mining
Department Department of Industrial Engineering and Management
Supervisors Professor Avigdor Gal
Professor Emeritus Avishai Mandelbaum
Abstract
Business processes are supported by information systems that record process-related events in event logs. Process mining aims at discovering useful information about the business process from these event logs. Process mining can be viewed as the link that connects process analysis fields (e.g. business process management and operations research) to data analysis fields (e.g. machine learning and data mining).
Process mining techniques aim at answering operational questions such as `does the executed process as observed in the event log correspond to what was planned?', `how long will it take for a running case to finish?' and `how should resource capacity or staffing levels change to improve the process with respect to some cost criteria?
Prior to this thesis, process mining techniques overlooked dependencies between cases when answering such operational questions. For example, state-of-the-art methods for predicting remaining times of running cases considered only historical data of the case itself, while the interactions among cases (e.g. through queueing for shared resources) were neglected.
In service processes in healthcare, banking, transportation etc., multiple customer-resource interactions occur, and customers often compete over scarce resources. Consequently, the central argument of this thesis is that for service-oriented processes, process mining solutions must consider case interactions when answering operational questions.
The main contribution of this research thesis is the start of bridging a noticeable gap between process mining and queuing theory. To this end, we introduce queue mining (a term coined in this thesis), which is a set of data-driven methods (models and algorithms) for queueing analysis of business processes.
Our queue mining techniques address the problems of prediction (delays and total times in the process), conformance to schedule (planned vs. actual), and process improvement (via production policy optimization). We demonstrate the effectiveness of these techniques with experiments on real-world data that comes from three domains: banking (a bank's call center), transportation (city buses), and healthcare (an outpatient hospital).
http://www.graduate.technion.ac.il/theses/Abstracts.asp?Id=29534
Presentation on theme: "Service Perspectives in Process Mining"— Presentation transcript:
https://slideplayer.com/slide/14748344/
Process Mining Presentations
Prof. Vil wan der Aalst
https://www.slideshare.net/wvdaalst/process-mining-chapter03datamining
About Process Mining - 2018
https://medium.com/@pedrorobledobpm/process-mining-plays-an-essential-role-in-digital-transformation-384839236bbe
Process Mining for Six Sigma: Utilising Digital Traces
I.Kregel D.Stemann J.Kochc A.Coners
Computers & Industrial Engineering
Available online 24 December 2020, 107083
In Press, Journal Pre-proof
Computers & Industrial Engineering
https://www.sciencedirect.com/science/article/abs/pii/S0360835220307531
Machine Learning in Manufacturing and Industry 4.0 applications
Using process mining to improve productivity in make-to-stock manufacturing
Rafael Lorenz,Julian Senoner,Wilfried Sihn &Torbjørn Netland
International Journal of Production Research
Volume 59, 2021 - Issue 16
https://www.tandfonline.com/doi/full/10.1080/00207543.2021.1906460
Ud. 15.10.2022, 2.4.2022, 2.2.2022
pub: 31.12.2020
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