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Bridge project

Business Transformation and Organisational AI-based Decision Making

DIREC project

Business Transformation and Organisational AI-based Decision Making

Summary

Today, business processes in private companies and public organisations are widely supported by Enterprise Resource Planning, Business Process Management and Electronic Case Management systems, put into use with the aim to improve efficiency of the business processes. 
 


The combined result is however often an increasingly elaborate information systems landscape, leading to ineffectiveness, limited understanding of business processes, inability to predict and find the root cause of losses, errors and fraud, and inability to adapt the business processes. This lack of understanding, agility and control over business processes places a major burden on the companies and organisations. 
 


Together with industry, the project aims to develop methods and tools that enable industry to develop new efficient solutions for exploiting the huge amount of business data generated by enterprise systems, with specific focus on tools and responsible methods for the use of process insights for business intelligence and transformation.

 

Project period: 2021-2025
Budget: DKK 16,8 million

Enterprise systems generate a plethora of highly granular data recording their operation. Machine learning has a great potential to aid in the analysis of this data in order to predict errors, detect fraud and improve their efficiency. Knowledge of business processes can also be used to support the needed transformation of old and heterogeneous it landscapes to new platforms. Application areas include Anti-Money-Laundering (AML) and Know-Your-Customer (KYC) supervision of business processes in the financial sector, supply chain management in agriculture and foodstuff supply, and compliance and optimisation of workflow processes in the public sector.

The research aim of the project to develop methods and tools that enable industry to develop new efficient solutions for exploiting the huge amount of business data generated by enterprise systems, with specific focus on tools and responsible methods for the use of process insights for business intelligence and transformation. Through field studies in organizations that are using AI, BPM and process mining techniques it will be investigated how organizations implement, use and create value (both operational and strategic) through AI, BPM and process mining techniques. In particular, the project will focus on how organizational decision-making changes with the implementation of AI-based algorithms in terms of decision making skills (intuitive + analytical) of the decision makers, their roles and responsibilities, their decision rights and authority and the decision context.

Scientific value

The scientific value of the project is new methods and user interfaces for decision support and business transformation and associated knowledge of their performance and properties in case studies. These are important contributions to provide excellent knowledge to Danish companies and education programs within AI for business innovation and processes.

Capacity building

For capacity building the value of the project is to educate 1 industrial PhD in close collaboration between CBS, DIKU and the industrial partner DCR Solutions. The project will also provide on-line course material that can be used in existing and new courses for industry, MSc and PhD.

Business and societal value

For the business and societal value, the project has very broad applicability, targeting improvements in terms of effectiveness and control of process aware information systems across the private and public sector. Concretely, the project considers cases of customers of the participating industry partners within the financial sector, the public sector and within energy and building management. All sectors that have vital societal role. The industry partner will create business value of estimated 10-20MDkr increased turnaround and 2-3 new employees in 5-7 years through the generation of IP by the industrial researcher and the development of state- of-the-art proprietary process analysis and decision support tools.

Impact

The project will develop methods and tools that enable industry to develop new efficient solutions for exploiting the huge amount of business data generated by enterprise systems.

Participants

Project Manager

Arisa Shollo

Associate Professor

Copenhagen Business School
Department of Digitalization

E: ash.digi@cbs.dk

Thomas Hildebrandt

Professor

University of Copenhagen
Department of Computer Science

Raghava Mukkamala

Associate Professor

Copenhagen Business School
Department of Digitalization

Morten Marquard

Founder & CEO

DCR Solutions

Søren Debois

CTO

DCR Solutions

Panagiotis Keramidis

PhD Student

Copenhagen Business School
Department of Digitalization

Partners

Categories
Bridge project

AI and Blockchains for Complex Business Processes

DIREC project

AI and Blockchains for Complex Business Processes

Summary

Today, business processes in private companies and public organizations are widely supported by Enterprise Resource Planning, Business Process Management, and Electronic Case Management systems to improve the efficiency of the processes. 

The combined result is however often an increasingly elaborate information systems landscape, leading to ineffectiveness, limited understanding of business processes, inability to predict and find the root cause of losses, errors, and fraud, and inability to adapt the business processes. This lack of understanding, agility and control over business processes places a major burden on companies and organizations. 

Together with industry, this project aims to develop methods and tools that enable the industry to develop new efficient solutions for exploiting the huge amount of business data generated by enterprise and blockchain systems, with specific focus on tools and responsible methods for the use of process insights for business intelligence and transformation.  

 

Project period: 2021-2025

Enterprise and block chain systems generate a plethora of highly granular data recording their operation. Machine learning has a great potential to aid in the analysis of this data in order to predict errors, detect fraud and improve their efficiency. Knowledge of business processes can also be used to support the needed transformation of old and heterogeneous it landscapes to new platforms. Application areas include Anti-Money-Laundering (AML) and Know-Your-Customer (KYC) supervision of business processes in the financial sector, supply chain management in agriculture and foodstuff supply, and compliance and optimisation of workflow processes in the public sector.

The research aim of the AI and Blockchain for Complex Business Processes project is methods and tools that enable industry to develop new efficient solutions for exploiting the huge amount of business data generated by enterprise and blockchain systems, from techniques for automatic identification of business events, via the development of new rule based process mining technologies to tools for the use of process insights for business intelligence and transformation.

The project will do this through a unique bridge between industry and academia, involving two innovative, complementary industrial partners and researchers across disciplines of AI, software engineering and business intelligence from three DIREC partner universities. Open source release (under the LGPL 3.0 license) of the rule-based mining algorithms developed by the PhD assigned task 2 will ensure future enhancement and development by the research community, while simultaneously providing businesses the opportunity to include them in proprietary software.

Impact

The project will develop methods and tools that enable the industry to develop new efficient soluations for exploiting the huge amout of business data generated by entreprise systems. 

News / coverage

Participants

Project Manager

Tijs Slaats

Associate Professor

University of Copenhagen
Department of Computer Science

E: slaats@di.ku.dk

Jakob Grue Simonsen

Professor

University of Copenhagen
Department of Computer Science

Thomas Hildebrandt

Professor

University of Copenhagen
Department of Computer Science

Michel Avital

Professor

Copenhagen Business School
Department of Digitalization

Henrik Axelsen

PHD Fellow

University of Copenhagen
Department of Computer Science

Christoffer Olling Back

Staff Machine Learning Engineer

ServiceNow

Anders Mygind

Director

ServiceNow

Søren Debois

Associate Professor

IT University of Copenhagen
Department of Computer Science

Omri Ross

Chief Blockchain Scientist

eToro

Axel Fjelrad Christfort

PhD Fellow

University of Copenhagen
Dept. of Computer Science

Hugo López

Associate Professor

Technical University of Denmark
DTU Compute

Partners