2023, Ehrenthal, Joachim, Hanne, Thomas, Telesko, Rainer, Gachnang, Phillip

Zu wenig Personal und Rollmaterial, kurzfristig angesagte Arbeiten an der Infrastruktur mit den entsprechenden betrieblichen Behinderungen und Einschränkungen sowie kurzfristig auftretende Störungen prägen zurzeit die Berichterstattung über die Entwicklungen im öffentlichen Verkehr der Schweiz. Es ist absehbar, dass sich diese unbefriedigende Situation über eine längere Zeitspanne kaum massgeblich verbessern wird. Umso wichtiger ist es, vorhandene Ressourcen optimal einzusetzen und den zukünftigen Bedarf an Mitarbeitenden und Rollmaterial in den Griff zu kriegen. Die Fachhochschulen der Ostschweiz (OST) und der Nordwestschweiz FHNW entwickelten mit der Südostbahn (SOB), den luxemburgischen Eisenbahnen (CFL) und der Eisenbahn-Softwareherstellerin Qnamic eine zukunftsweisende Software zur Unterstützung der Eisenbahn-Disposition, um in Echtzeit über situationsspezifische Massnahmenpakete zur Störungsbehebung zu verfügen.

2021, Pustulka, Elzbieta, Hanne, Thomas, Gachnang, Phillip, Biafora, Pasquale, Arai, Kohei, Kapoor, Supriya, Bhatia, Rahul

Information extraction (IE) from forms remains an unsolved problem, with some exceptions, like bills. Forms are complex and the templates are often unstable, due to the injection of advertising, extra conditions, or document merging. Our scenario deals with insurance forms used by brokers in Switzerland. Here, each combination of insurer, insurance type and language results in a new document layout, leading to a few hundred document types. To help brokers extract data from policies, we developed a new labeling method, called FLIE (form labeling for information extraction). FLIE first assigns a document to a cluster, grouping by language, insurer, and insurance type. It then labels the layout. To produce training data, the user annotates a sample document by hand, adding attribute names, i.e. provides a mapping. FLIE applies machine learning to propagate the mapping and extracts information. Our results are based on 24 Swiss policies in German: UVG (mandatory accident insurance), KTG (sick pay insurance), and UVGZ (optional accident insurance). Our solution has an accuracy of around 84-89%. It is currently being extended to other policy types and languages.

2021, Gupta, Varun, Hanne, Thomas, Telesko, Rainer, Silhavy, Radek

This exploratory case study was conducted with five IT startups in order to investigate how requirement engineering-related activities are performed and what is the state of maturity with the practices & tools used. Another objective was to study that how the startups managed their practices during Corona Virus (COVID19) pandemic time. The results indicate that flexibility and access to the online tools were the main strengths of the startups to cope up with the pandemic situation while fluctuating market demands affected them marginally. The startups do vary in domain, team size, practices and selection of the tools, with matured startups having more structured (but flexible) processes compared to younger startups. The young startups have the opportunity to learn from the practices of the matured startups, to adopt the learning in their working context. The previous software development experience of the startups and its founders does affect the maturity of the practices and selection of the tools. The flexibility and agility as evident in the working context of the startups helped them to turn pandemic situation into their business opportunities.

2021, Yazdi, Amir Karbassi, Hanne, Thomas, Osorio Gómez, Juan Carlos

Purpose - The aim of this paper is to find and prioritise multiple critical success factors (CSFs) for the implementation of LSS in the oil and gas industry. Design/methodology/approach - Based on a preselected list of possible CFSs, experts are involved in screening them with the Delphi method. As a result, 22 customised CSFs are selected. To prioritise these CSFs, the step-wise weight assessment ratio analysis (SWARA) method is applied to find weights corresponding to the decision-making preferences. Since the regular permutation-based weight assessment can be classified as NP-hard, the problem is solved by a metaheuristic method. For this purpose, a genetic algorithm (GA) is used. Findings - The resulting prioritisation of CSFs helps companies find out which factors have a high priority in order to focus on them. The less important factors can be neglected and thus do not require limited resources. Research limitations/implications - Only a specific set of methods have been considered. Practical implications - The resulting prioritisation of CSFs helps companies find out which factors have a high priority in order to focus on them.Social implicationsThe methodology supports respective evaluations in general. Originality/value - The paper contributes to the very limited research on the implementation of LSS in the oil and gas industry, and, in addition, it suggests the usage of SWARA, a permutation method and a GA, which have not yet been researched, for the prioritisation of CSFs of LSS.

2022, Hanne, Thomas, Gachnang, Phillip, Gatziu Grivas, Stella, Kirecci, Ilyas, Schmitter, Paul

In this paper, we discuss the possibility of using machine learning (ML) to specify and validate maturity models, in particular maturity models related to the assessment of digital capabilities of an organization. Over the last decade, a rather large number of maturity models have been suggested for different aspects (such as type of technology or considered processes) and in relation to different industries. Usually, these models are based on a number of assumptions such as the data used for the assessment, the mathematical formulation of the model and various parameters such as weights or importance indicators. Empirical evidence for such assumptions is usually lacking. We investigate the potential of using data from assessments over time and for similar institutions for the ML of respective models. Related concepts are worked out in some details and for some types of maturity assessment models, a possible application of the concept is discussed.

2021, Amiti, Taulant, Karimi, Mohammad Ali, Wüthrich, Benjamin, Hanne, Thomas, Silhavy, Radek, Silhavy, Petr, Prokopova, Zdenka

2021, Oller, Heide, Dornberger, Rolf, Hanne, Thomas, Thampi, Sabu M., Krishnan, Sri, Hegde, Rajesh M., Ciuonzo, Domenico, Hanne, Thomas, Kannan R., Jagadeesh

2021, Meier, Danny, Keller, Benjamin, Kolb, Markus, Hanne, Thomas, Dorronsoro, Bernabé, Amodeo, Lionel, Pavone, Mario, Ruiz, Patricia

2021, Moser, Lars, Saner, Kevin, Oggier, Vincent, Hanne, Thomas, Arai, Kohei

2021, Wild, Simon, Parlar, Soyhan, Hanne, Thomas, Dornberger, Rolf

This paper analyses how the required skills in a job post can be extracted. With an automated extraction of skills from unstructured text, applicants could be more accurately matched and search engines could provide better recommendations. The problem is optimized by classifying the relevant parts of the description with a multinomial naïve Bayes model. The model identifies the section of the unstructured text in which the requirements are stated. Subsequently, a named entity recognition (NER) model extracts the required skills from the classified text. This approach minimizes the false positives since the data which is analyzed is already filtered. The results show that the naïve Bayes model classifies up to 99% of the sections correctly, and the NER model extracts 65% of the skills required for a position. The accuracy of the NER model is not sufficient to be used in production. On the validation set, the performance was insufficient. A more consistent labelling guideline would be needed and more data should be annotated to increase the performance.