A Business Process Miner for Industry: A Genetic Programming Based Tool

Lead Research Organisation: Cranfield University
Department Name: Sch of Applied Sciences

Abstract

Business processes are becoming ever more complex. Managers need to have an accurate picture on how a business process is operating in a live environment and guidance on how a process can be improved. For some time Enterprise Resource Planning (ERP) software products have been able to record execution data for an organisation's live hosted processes. Such data typically contains detail on process tasks and the times at which they are executed. It is possible to manually reconstruct a flow chart of a process from this data showing how the tasks link together; however, this is a time consuming and error prone task. Automated process mining methods have been proposed by academic groups though commercial implementation of process mining solutions is still at a very early stage. While there is growing corporate awareness for the need for automated process mining techniques for re-engineering initiatives, current practice is still expert driven, requiring manual problem detection and resolution.The aim of process mining is to identify and extract process patterns from data logs to reconstruct an overall process flowchart. The data logs, more commonly known in the business process field as event logs, contain execution data for a live process. Such event logs may be hosted within ERP systems, Business Process Management (BPM) systems and workflow systems, owned by medium and large organisations, recording the task by task completion of computer assisted processes. The technique outlined in this proposal can mine process logs and identify the key features in a process. Process executions that do not conform to these features can also be mined. In this way, variations in the way a process is executed can be detected. This differs from current process mining techniques that aim to show only the 'correct' execution of a process. The functionality of the proposed technique is of benefit to organisations wishing to model complex process flows and specifically identify departures from normal process execution. Some beneficiaries are:* Online Retailers / to analyse the ordering process a customer must complete in the purchase of goods and services.* Financial Institutions / for the detection of fraud through the identification of suspicious process execution traces.* Call Centres / to check if essential parts of a process are being bypassed.

Publications

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C Turner (First Author) (2009) Mining Process Flowcharts from Business Data: An Evolutionary Approach in 6th International Conference on Digital Enterprise Technology (DET-2009)

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C Turner (First Author) (2009) Process Mining: An Application for Industry in 11th International Conference on the Modern Information Technology in the Innovation Processes of the Industrial Enterprise (MITIP-2009)

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J Mehnen (First Author) (2010) Business Process Mining for Industry: Successes and Caveats in 7th CIRP International Conference on Intelligent Computation in Manufacturing Engineering (ICME-2010)

 
Description Business processes are becoming ever more complex. Managers need to have an accurate picture on how a business process is operating in a live environment and guidance on how a process can be improved. For some time, Enterprise Resource Planning (ERP) software products have been able to record execution data for an organisation's live hosted processes. Such data typically contains detail on process tasks and the times at which they are executed. It is possible to manually reconstruct a flow chart of a process from this data showing how the tasks link together; however, this is a time consuming and error prone task. Automated process mining methods have been proposed by academic groups though commercial implementation of process mining solutions is still at a very early stage. While there is growing corporate awareness for the need for automated process mining techniques for re-engineering initiatives, current practice is still expert driven, requiring manual problem detection and resolution.

The aim of process mining is to identify and extract process patterns from data logs to reconstruct an overall process flowchart. The data logs, more commonly known in the business process field as event logs, contain execution data for a live process. Such event logs may be hosted within ERP systems, Business Process Management (BPM) systems and workflow systems, owned by medium and large organisations, recording the task by task completion of computer assisted processes.
Exploitation Route The technique developed in this project can mine process logs and identify the key features in a process. Process executions that do not conform to these features can also be mined. In this way, variations in the way a process is executed can be detected. This differs from current process mining techniques that aim to show only the 'correct' execution of a process. The functionality of the developed technique is of benefit to organisations wishing to model complex process flows and specifically identify departures from normal process execution. Some beneficiaries are:

- Online Retailers / to analyse the ordering process a customer must complete in the purchase of goods and services.

- Financial Institutions / for the detection of fraud through the identification of suspicious process execution traces.

- Call Centres / to check if essential parts of a process are being bypassed.

Routes to Market: A number of revenue raising vehicles (including the end user licensing, online service, consultancy and software vendor licensing) were investigated and assessed as commercialisation opportunities and routes to market for the developed tool. The market analysis report guided the selection of the consultancy option as the most effective commercialisation route for this project. The consultancy route offers a low cost entry to market with the potential for a substantial financial return in the medium to long term. An initial commercialisation strategy, covering route to market and intellectual property, was developed with the help of Cranfield University Business Development Directorate.

Business Events: An open day presentation was organised to help in networking and marketing the software. The project team participated in 4 industry focused seminars/conferences. The principal investigator also presented a poster on this project in the EPSRC Theme Day in Manufacturing Research (15 April 2010, London). Marketing brochures/posters on the project were produced and a website was used as a medium to promote and distribute the publications and results arising from the project.
Sectors Digital/Communication/Information Technologies (including Software),Manufacturing, including Industrial Biotechology

 
Description The technique developed in this project can mine process logs and identify the key features in a process. Process executions that do not conform to these features can also be mined. In this way, variations in the way a process is executed can be detected. This differs from current process mining techniques that aim to show only the 'correct' execution of a process. The functionality of the developed technique is of benefit to organisations wishing to model complex process flows and specifically identify departures from normal process execution. Some beneficiaries are: (i) online retailers / to analyse the ordering process a customer must complete in the purchase of goods and services, (ii) financial institutions / for the detection of fraud through the identification of suspicious process execution traces, and (iii) call centres / to check if essential parts of a process are being bypassed.
First Year Of Impact 2011
Sector Digital/Communication/Information Technologies (including Software),Manufacturing, including Industrial Biotechology
Impact Types Societal,Economic