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Die Telekommunikationsindustrie hat in den letzten Jahrzehnten einen enormen Wandel vollzogen. Für Telekommunikationsunternehmen erfordert dies fundamentale Umstrukturierungen von Strategie, Prozessen, Anwendungssystemen und Netzwerktechnologien. Dabei spielen Unternehmensarchitekturen und Referenzmodelle eine wichtige Rolle. Zwar existieren in der Praxis anerkannte Referenzmodelle, aber wie sind diese für eine systematische Transformation zu gestalten? Wie sieht eine konkrete Lösung für die Telekommunikationsindustrie aus?
Als Antwort stellt Christian Czarnecki in seinem Buch eine referenzmodellbasierte Unternehmensarchitektur vor. Basierend auf einer umfangreichen Untersuchung von Transformationsprojekten werden Probleme und Anforderungen der Praxis identifiziert, für die mit Methoden der Unternehmenstransformation, Referenzmodellierung und Unternehmensarchitektur ein Lösungsvorschlag entwickelt und evaluiert wird. Dieser besteht u. a. aus detaillierten Anwendungsfällen, Referenzprozessabläufen, einer Zuordnung von Prozessen zu Anwendungssystemen sowie Handlungsempfehlungen zur Virtualisierung.
Für Wissenschaftler und Studierende der Wirtschaftsinformatik zeigt das Buch neue Erkenntnisse einer anwendungsorientierten Referenzmodellierung. Für Praktiker liefert es eine methodisch fundierte Lösung für die aktuellen Transformationsbedarfe der Telekommunikationsindustrie. Christian Czarnecki arbeitet seit 2004 als Unternehmensberater und hat viele Telekommunikationsunternehmen bei deren Transformation begleitet. In 2013 erfolgte die Promotion zum Doktoringenieur an der Otto-von-Guericke-Universität Magdeburg.
Because of customer churn, strong competition, and operational inefficiencies, the telecommunications operator ME Telco (fictitious name due to confidentiality) launched a strategic transformation program that included a Business Process Management (BPM) project. Major problems were silo-oriented process management and missing cross-functional transparency. Process improvements were not consistently planned and aligned with corporate targets. Measurable inefficiencies were observed on an operational level, e.g., high lead times and reassignment rates of the incident management process.
Intelligent autonomous software robots replacing human activities and performing administrative processes are reality in today’s corporate world. This includes, for example, decisions about invoice payments, identification of customers for a marketing campaign, and answering customer complaints. What happens if such a software robot causes a damage? Due to the complete absence of human activities, the question is not trivial. It could even happen that no one is liable for a damage towards a third party, which could create an uncalculatable legal risk for business partners. Furthermore, the implementation and operation of those software robots involves various stakeholders, which result in the unsolvable endeavor of identifying the originator of a damage. Overall it is advisable to all involved parties to carefully consider the legal situation. This chapter discusses the liability of software robots from an interdisciplinary perspective. Based on different technical scenarios the legal aspects of liability are discussed.
Automated driving is now possible in diverse road and traffic conditions. However, there are still situations that automated vehicles cannot handle safely and efficiently. In this case, a Transition of Control (ToC) is necessary so that the driver takes control of the driving. Executing a ToC requires the driver to get full situation awareness of the driving environment. If the driver fails to get back the control in a limited time, a Minimum Risk Maneuver (MRM) is executed to bring the vehicle into a safe state (e.g., decelerating to full stop). The execution of ToCs requires some time and can cause traffic disruption and safety risks that increase if several vehicles execute ToCs/MRMs at similar times and in the same area. This study proposes to use novel C-ITS traffic management measures where the infrastructure exploits V2X communications to assist Connected and Automated Vehicles (CAVs) in the execution of ToCs. The infrastructure can suggest a spatial distribution of ToCs, and inform vehicles of the locations where they could execute a safe stop in case of MRM. This paper reports the first field operational tests that validate the feasibility and quantify the benefits of the proposed infrastructure-assisted ToC and MRM management. The paper also presents the CAV and roadside infrastructure prototypes implemented and used in the trials. The conducted field trials demonstrate that infrastructure-assisted traffic management solutions can reduce safety risks and traffic disruptions.