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An approach for analyzing business process execution complexity based on textual data and event log
(2023)
With the advent of digital transformation, organizations increasingly rely on various information systems to support their business processes (BPs). Recorded data, including textual data and event log, expand exponentially, complicating decision-making and posing new challenges for BP complexity analysis in Business Process Management (BPM). Herein, Process Mining (PM) serves to derive insights based on historic BP execution data, called event log. However, in PM, textual data is often neglected or limited to BP descriptions. Therefore, in this study, we propose a novel approach for analyzing BP execution complexity by combining textual data serving as an input at the BP start and event log. The approach is aimed at studying the connection between complexities obtained from these two data types. For textual data-based complexity, the approach employs a set of linguistic features. In our previous work, we have explored the design of linguistic features favorable for BP execution complexity prediction. Accordingly, we adapt and incorporate them into the proposed approach. Using these features, various machine learning techniques are applied to predict textual data-based complexity. Moreover, in this prediction, we show the adequacy of our linguistic features, which outperformed the linguistic features of a widely-used text analysis technique. To calculate event log-based complexity, the event log and relevant complexity metrics are used. Afterward, a correlation analysis of two complexities and an analysis of the significant differences in correlations are performed. The results serve to derive recommendations and insights for BP improvement. We apply the approach in the IT ticket handling process of the IT department of an academic institution. Our findings show that the suggested approach enables a comprehensive identification of BP redesign and improvement opportunities.
Layered van der Waals ferromagnets, which preserve their magnetic properties down to exfoliated monolayers, are fueling an abundance of fundamental research and nanoscale device demonstration. CrGeTe3 is a prime example of this class of materials. Its temperature-pressure phase diagram features an insulator-to-metal transition and a significant increase in ferromagnetic Curie-Weiss temperatures upon entering the metallic state. We use density functional theory to understand the magnetic exchange interactions in CrGeTe3 at ambient and elevated pressures. We calculate Heisenberg exchange couplings, which provide the correct ferromagnetic ground state and explain the experimentally observed pressure dependence of magnetism in CrGeTe3. Furthermore, we combine density functional theory with dynamical mean-field theory to investigate the effects of electronic correlations and the nature of the high-pressure metallic state in CrGeTe3.
Large three-dimensional metallic parts can be printed layer-by-layer using gas metal arc directed energy deposition (GMA-DED) process at a high deposition rate and with little or no material wastage. Fast responsive real-time monitoring of GMA-DED process signatures and their transient variations is required for printing of dimensionally accurate and structurally sound parts. A systematic experimental investigation is presented here on multi-layer GMA-DED with two different scanning strategies using a high strength low alloy (HSLA) filler wire. The dynamic metal transfer, melt pool temperature field and its longitudinal cross-section, and arc voltage and current are monitored synchronously. The transient arc heat input and the melt pool solidification cooling rate are estimated from the monitored signals. The layer-wise variations of the melt pool dimension, surface temperature profile, thermal cycles, and solidification cooling rate are examined for different scanning strategies. It is comprehended that the part defects can be minimized, and the mass production of zero-defect parts can be achieved in GMA-DED process with synchronized monitoring and assessment of the real-time process signatures.
Unternehmen und Unternehmens-IT stehen im Zeitalter der Digitalisierung vor großen Umbrüchen. Geschäftsprozesse und die eingesetzten IT-Systemlandschaften verändern sich im schnellen Takt, teilweise werden erprobte Geschäftsmodelle disruptiv durch völlig neue Technologien ergänzt oder gar ersetzt. Dieses schnelllebige Umfeld erfordert eine intensive und qualitativ hochwertige Ausbildung der Mitarbeiter. Präsenzschulungen sind in Zeiten der Corona-Pandemie aber schwer zu realisieren. Wie eine praxisnahe und Fallstudien-basierte Ausbildung im ERP-Kontext trotzdem möglich ist, zeigt das E-Learning-basierte Programm erp4students der Technischen Hochschule Brandenburg am Beispiel von 13 Modulen im Umfeld neuester SAP-Technologien.