AI Combined with Optimization Techniques in Measuring and Predicting Business Performance
Ali Emrouznejad, Business Analytics, University of Surrey, United Kingdom
Distributed and Hybrid Digital Twins for Low Latency Applications: The Pros of Exploiting Edge Cloud Computing and the Challenges for Simulation
Paolo Bellavista, Alma Mater Studiorum, Università di Bologna, Italy
Process Mining for Interdisciplinary Research
Agnes Koschmider, University of Bayreuth, Germany
AI Combined with Optimization Techniques in Measuring and Predicting Business Performance
Ali Emrouznejad
Business Analytics, University of Surrey
United Kingdom
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Brief Bio
Ali Emrouznejad is a Professor and Chair in Business Analytics at Surrey Business School, UK. He is also director of the Centre for Business Analytics in Practice, where he leads research efforts in a variety of areas, including performance measurement and management, efficiency and productivity analysis, and AI and big data. He earned his MSc in Applied Mathematics and his PhD in Operational Research and Systems from Warwick Business School, UK. He has been named as one of the top 2% most influential scientists in the world by Stanford University. He is a Fellow of the Institute of Mathematics and its Applications (FIMA) Fellow of the Institute Sustainability as well as Fellow of the Institute for People-Centred Artificial Intelligence. Prof Emrouznejad has also collaborated on various research projects that were funded by reputable organizations such as the Department for Education and Skills (DfES), Royal Academy of Engineering, British Council, Knowledge Transfer Partnership (KTP), WHO-Africa (World Health Organization), European Unions (Regional Development Funds), among others. Prof Emrouznejad has published over 250 articles in top-ranked journals and has authored or edited several books. Additionally, he serves as an editor, associate editor, or member of the editorial boards for multiple scientific journals. [see: https://emrouznejad.com/].
Abstract
The application of AI and optimization techniques in measuring and predicting efficiency and productivity has become essential for driving operational excellence in business performance. These advanced methodologies enable organizations to analyse extensive datasets, identify inefficiencies, and areas of improvement within their processes. By utilizing AI algorithms such as neural network and optimization models such as data envelopment analysis, predictive models can forecast future performance based on historical data, empowering proactive decision-making and optimized resource allocation to enhance productivity. Through the strategic implementation of AI and optimization, businesses can streamline operations, minimize resources, and maximize outputs and ultimately achieve higher levels of operational excellence.
In this talk, we will explore the application of data envelopment analysis (DEA) in measuring the efficiency of organizations. Additionally, we will investigate how the obtained results can be used to feed AI algorithms, enabling us to identify and explain the sources of inefficiency and leverage these findings to predict performance.
Distributed and Hybrid Digital Twins for Low Latency Applications: The Pros of Exploiting Edge Cloud Computing and the Challenges for Simulation
Paolo Bellavista
Alma Mater Studiorum, Università di Bologna
Italy
https://www.unibo.it/sitoweb/paolo.bellavista
Brief Bio
Paolo Bellavista is a Full Professor of mobile and distributed systems at the University of Bologna, Italy. His primary research interests include middleware for mobile computing, digital twins for Industry4.0 and smart city applications, QoS management in the cloud continuum, infrastructures for big data processing in industrial environments, and performance optimization in wide-scale and latency-sensitive deployment scenarios. Related to digital twins, he was the scientific coordinator of the H2020 IoTwins project.
Abstract
Digital twins are becoming a crucial tool for both design purposes (e.g., dimensioning before implementation – offline digital twins) and efficiency goals (e.g., online reconfiguration to improve quality – online digital twins), by posing several, still open, technical challenges to their effective implementation. In particular, the keynote speech will focus on the emerging directions of hybrid (synergically exploiting simulations and data-driven machine learning models) and distributed (running also in cloud continuum edge nodes, e.g., for federated learning and efficient after-training operations) digital twins. Practical examples of implemented testbeds with edge cloud nodes supporting digital twins will be described from the IoTwins H2020 project, in the vertical domains of industrial manufacturing plants and smart city management optimization. Moreover, the speech will present our original recent research work on the design and implementation of a novel simulation tool for vehicular applications, which can exploit ETSI MEC-compliant edge cloud nodes in proximity.
Process Mining for Interdisciplinary Research
Agnes Koschmider
University of Bayreuth
Germany
Brief Bio
Agnes Koschmider is Full Professor of Business Informatics at the University of Bayreuth and has a leading position in the Business Informatics branch of the Fraunhofer FIT. From 2019 to 2022 Agnes Koschmider was professor of business informatics at the Computer Science Institute of the University of Kiel. She completed her PhD in 2007 and her habilitation in Applied Informatics in 2015 at Karlsruher Institute of Technology (KIT).
She researches methods for data-driven analysis and explanation of processes (process mining), based on artificial intelligence, and methods for predicting process behavior. She is also researching on methods for privacy-preserving analysis and minimizing the re-identification of process data. At the center of her research is process analytics: developing a pipeline to efficiently process the complete chain from raw data (time series, sensor event data, and video data) to process discovery. The applications of such a data pipelines can be found in many disciplines such as medicine, agricultural sciences, geology, geography, material sciences or marine sciences.
Abstract
Process mining allows discovering bottlenecks in processes and revealing the deviations between real-life processes and to-be one. Process mining usually focuses on processing discrete event data, typically at the business level. The increasing volume of data demands techniques that can identify cause-effects in the data. Process mining promises to discover valuable knowledge from different types of data in terms of identifying anomalies in the processes and even explaining new effects within the data. For this purpose, however, existing process mining techniques must be adapted in order to meet the requirements of the other disciplines.
The first part of my talk outlines new fields of application for process mining and summarizes requirements for new process mining techniques. The second part of my talk shows implementations of the processing of various data types from raw data to processes and to AR visualization.