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Apromore Pty Ltd and the University of Melbourne jointly developed a research-driven and open-source process-mining software which served as the core for a commercial enterprise solution. By leveraging an organisation’s pre-existing data, the software discovers and simulates processes to help businesses achieve excellence in process efficiency.
- Apromore Pty Ltd develops process-mining software for enterprise organisations across many industries, helping them to discover ineffective processes and improve operations
- The University of Melbourne start-up company has continued a close collaboration with the University and industry partners
- Apromore was established to commercialise the culmination of over a decade of research – a sophisticated, system-neutral process-mining platform with longevity, thanks to its investment in University of Melbourne research into the future
- German software vendor GBTEC, Australian consulting and technology firm Leonardo, and the University of Melbourne contributed a combined $A6.8 million to Apromore through a Series A investment. Apromore has received strong interest from brand name investors in preparation for their Series B
- GBTEC has partnered with Apromore to include the process-mining platform as part of their business process modelling and automation software – and Apromore is expanding its international operations after securing significant contracts in the finance sector.
Apromore Pty Ltd has developed software that’s helping businesses leverage the full power of their data to achieve excellence in operation, performance and compliance.
Backed by world-leading research, the process-mining software gained its start in an open-source version developed at the University of Melbourne. Now, its commercial enterprise solution is recognised as one of the top solutions on the market – compatible with a vast number of software programs and effective across many industries.
Apromore Pty Ltd was co-founded in September 2019 by University of Melbourne researcher Professor Marcello La Rosa. It raised $A6.84 million in its first round of fundraising from investors, including GBTEC, Leonardo and the University of Melbourne.
The start-up’s Global Marketing Manager Tamarie Ellis said that in 2022, Apromore secured clients in the financial and other sectors, both in Australia and internationally, leading to an expansion of operations in the UK and Europe.
“Our clients like the idea that our software is based on research from the University of Melbourne, because process-mining is an emerging science. They want to know they are investing in a solution that will meet their needs into the future, and our continued partnership with the University will enable Apromore to achieve the full potential of process-mining as it evolves.”
All organisations experience inefficiencies in processes that lead to missed deadlines, lost opportunities and poor compliance, and these inefficiencies spawn even greater problems such as decreases in profits, growth and overall morale.
Traditionally, business processes are analysed by observing and interviewing the people who use them. This is labour intensive and error-prone.
Most organisations also use digital technologies that generate huge volumes of process data. Enterprise systems – such as customer relationship management software, payroll systems and project management software – track information such as what tasks are performed, by whom, and in what order.
Process-mining software takes the legwork and margin for error out of process analysis by extracting useful process data from a business’ enterprise systems. It finds bottlenecks, repetition and waste that affect business performance. It can also help businesses to assess how well they comply with service-level agreements, their own standard operating procedures or regulatory frameworks.
Apromore’s process-mining software is based on research led by Professor Marcello La Rosa. He began the project in 2009 at the Queensland University of Technology. Professor La Rosa joined the University of Melbourne in February 2018, bringing with him a team of nine researchers.
The research is a collaboration with Professor Marlon Dumas and his team from the University of Tartu, Estonia, as well as groups from other research institutions.
Together, they built the software using expertise in computer science, information systems, business process management, statistics, graph theory, data mining and machine learning. The software connects to a business’ enterprise systems and automatically collects and analyses data, looking for weak spots. Based on these analyses, the business can adapt its processes and then use the software to track the results.
Professor La Rosa says the Apromore software – so named because it started as an ‘Advanced Process Model Repository’ – is the only commercial software in the process intelligence space that is truly inspired and informed by the latest research and innovation.
“As organisations are achieving higher levels of process maturity, they demand more advanced, yet simple to use, capabilities to gain insights from their transactional data, in order to optimise their operations. Apromore combines these two aspects of ease of use and sophistication, making it particularly attractive as an alternative to other tools.”
As part of Apromore’s partnership with the University, researchers field-tested the software with customers in different sectors and provided feedback that helped to refine the software.
The research has been supported by over $A10 million from Australian and European funding schemes and from University of Melbourne seed funding.
Technology development history
The concept of process mining was introduced in academia in the early 2000s. Since then, no open-source software to analyse business processes had been developed for commercial use.
Addressing this gap in the market, Professor La Rosa, Professor Dumas and two co-founders launched Apromore in September 2019 to further develop and commercialise the software. Professor La Rosa is CEO of the company.
The intellectual property (IP) born from Professor La Rosa and his group’s research at the University of Melbourne was transferred to Apromore in return for equity. As part of the agreement, the University also invested in the company, and following its establishment, the University moved into a partnership role with the start-up.
“The University supported the development and commercialisation of the software through significant seed funding, IP transfer and commercialisation advice on how to best spin out the initiative. Over an 18-month period, many different approaches were assessed.”
In its Series A round, Apromore raised $A6.8 million from investors including the University, German software vendor GBTEC, and Australian consulting and technology firm Leonardo.
The Series A investment is being used to further develop the software for business use. In particular, the company will make the software more robust, scalable and secure. They will also add features to enable the software to automatically find ways to improve processes, simulate scenarios to help businesses plan, and identify and analyse the causes of problems.
GBTEC also entered a strategic partnership with Apromore to incorporate the process-mining technology as a core component in GBTEC’s business process modelling and automation software, BIC Platform.
Apromore offers two versions of its software. The Community Edition is free to download and use. Harnessing a collaborative approach, contributors from universities, research institutes and private organizations have added updates and fixes to the Community Edition over the past 10 years.
The Enterprise Edition, which includes support and advanced features, is available on subscription. Businesses can run the Enterprise Edition on premises, in the cloud or in a hybrid model.
The open-source core of Apromore is shared under a free-software license called a GNU Lesser General Public License (LGPL 3.0). This gives researchers and professionals the freedom to use, change and share the code.
ARC Discovery Project (DP180102839) ‘Diagnosis and prediction of business process deviances’
ARC Discovery Project (DP150103356) ‘Improved business decision-making via liquid process model collections’
ARC Discovery Project (DP110100091) ‘Risk-aware business process management’
ARC Linkage Project (LP110100252) ‘Facilitating business process standardisation and reuse’
Smart Services Cooperative Research Centre
European Research Council (ERC) Advanced Grant ‘PIX: Process Improvement Explorer’ to Professor Marlon Dumas
Estonian Research Council (IUT20-55) ‘Data-Driven Management of Business Processes’ to Professor Marlon Dumas
Melbourne School of Engineering Strategic Investment Fund, University of Melbourne
Reissner D et al (2020) Scalable alignment of process models and event logs: an approach based on automata and S-components. Information Systems, doi: 10.1016/j.is.2020.101561
Augusto A et al (2019) Automated discovery of process models from event logs: review and benchmark. IEEE Transactions on Knowledge and Data Engineering 31(4): 686–705. doi: 10.1109/TKDE.2018.2841877
La Rosa M et al (2011) APROMORE: An advanced process model repository. Expert Systems with Applications 38(6): 7029–7040. doi: 10.1016/j.eswa.2010.12.012
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First published on 27 July 2020.
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