IE Webinar Series continues in January
Informatics Europe has launched different webinar series since September 2021. The series will continue in January.
Click here to register for our new webinar scheduled on 18 January 2022.
Abstract:
Business Process Management (BPM) is a cross-disciplinary field of study at the intersection between Informatics, Industrial Engineering, and Management Science. The goal of BPM is to provide conceptual frameworks, methods, and tools to enable organizations to continuously monitor and improve the way they perform work, in order to fulfill the ever-changing expectations of their customers and other stakeholders.
A central activity in the field of BPM is business process redesign: Applying changes to a business process (a.k.a. interventions) with the aim of improving it with respect to one or more quantitative performance measures such as cycle time, cost, or defect rate. Examples of interventions include automating part of a business process, adding or re-deploying human resources, or changing the flow of activities in a process.
In this talk, we will discuss a decades-old problem in the field of BPM, namely "what-if process analysis". In simple terms, this problem can be posed as follows: How to reliably and accurately predict the impact of an intervention on a business process in terms of one or more business process performance measures? We will discuss the limitations of approaches based on discrete event simulation developed in the 1990s, which have been relatively successful in the context of repetitive manufacturing processes but have largely failed in the context of human-intensive processes. We will then present ongoing efforts to tackle this problem by combining observational data, experimental data, and domain knowledge using hybrid modeling methods drawing from the fields of discrete event simulation, machine learning, and causal inference.
About the speaker: Marlon Dumas is Professor of Information Systems at University of Tartu (Estonia) and co-founder of Apromore – a spin-off company that develops open-source solutions for process mining and optimization. His research focuses on data-driven methods for business process management, including process mining, predictive process monitoring and data-driven process simulation. He is recipient of an Advanced Grant from the European Research Council with the mission of developing algorithms for automated discovery and assessment of business process improvement opportunities from execution data.
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