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In-depth analysis of customer journeys to broaden the understanding of customer behaviors and expectations in order to improve the customer experience is considered highly relevant in modern business practices. Recent studies predominantly focus on retrospective analysis of customer data, whereas more forward-directed concepts, namely predictions, are rarely addressed. Additionally, the integration of robotic process automation (RPA) to potentially increase the efficiency of customer journey analysis is not discussed in the current field of research. To fill this research gap, this paper introduces “customer journey mining”. Process mining techniques are applied to leverage digital customer data for accurate prediction of customer movements through individual journeys, creating valuable insights for improving the customer experience. Striving for improved efficiency, the potential interplay of RPA and customer journey mining is examined accordingly. The research methodology followed is based on a design science research process. An initially defined customer journey mining artifact is operationalized through an illustrative case study. This operationalization is achieved by analyzing a log file of an online travel agency functioning as an orientation for researchers and practitioners while also evaluating the initially defined framework. The data is used to train seven distinct prediction models to forecast the touchpoint a customer is most likely to visit next. Gradient-boosted trees yield the highest prediction accuracy with 43.1%. The findings further indicate technical suitability for RPA implementation, while financial viability is unlikely.
Against the setting of an increasing need for innovation and low margins, companies in the logistics
sector are facing highly competitive pressure. One field with high potential for optimization lies within
damage quotas. The use of big data analytics or data mining represents a promising approach to face
this challenge. However, within supply chain management, data mining is hardly being researched on
regarding damage quotas and thus not being utilized to its full possible extend. At the current time it
seems to predominantly be used for route and utilization optimization while the analysis of delivery
damages is hardly considered.
The aim of this research is therefore to showcase an initial approach for data mining in logistics to predict
delivery damage probabilities and to validate this by means of a multiple case study research. To create
a sound basis for evaluation, the groundwork is laid out based on CRISP-DM by the analysis of reference
data (German road-cargo market).
As a central result it is noted that data mining can systematically be used to help reducing the damages
by forecasting the probabilities of damages occurring during transport in dependence of different factors.
The approach can be utilized across different markets as long as sufficient data tracking delivery
damages is being collected within a company. Challenges arise in the field of air- and sea-freight.
A novel approach for ventilation systems is a periodically varying supply air flow rate, the so-called unsteady mode of operation. So far, useful effects of this unsteady operating mode have been observed, but the effect mechanisms are still unknown. In this manuscript, simulations using the recently proposed k-ω-ζ - f model implemented in a sensitized RANS computational framework for a cuboid room with swirl diffusers are compared and validated with PIV measurements.
Wastewater Generation Model to Predict Impacts of Urine Separation on Wastewater Treatment Plants
(2023)
Wastewater treatment plants are under increasing pressure to enhance resource efficiency and reduce emissions into water bodies. Separation of urine within the catchment area may be an alternative to mitigate the need for costly expansions of central wastewater treatment plant. While previous investigations assumed a spatially uniform implementation of urine separation across the catchment area, the present study introduces a modelling framework which allows to determine the influence of targeted urine separation on the operation and emissions of central wastewater treatment plants. The framework includes an adapted stochastic wastewater generation model, the Stormwater Management Model, and Activated Sludge Model No. 3 with Bio-P module (SIMBA#). The entire application is embedded in the R programming language. The model is validated by an extensive sampling and measurement campaign. Preliminary results based on observed and simulated wastewater generation and transport for a catchment area of 436 residents indicate the suitability of the model for wastewater generation and transport modelling, but also show further need for calibration.
A communication over an Internet Protocol (IP) based network fails if an endpoint sends packets that are too big to reach their destination and if the sender is unable to detect that. The node on the path that drops these packets should respond with a Packet Too Big (PTB) message. However, multiple scenarios exist in which the sender will not receive a PTB message. Even if it does, it refrains from using the information in case it suspects that a potential attacker forged the message. In particular, we are not aware of any implementation of the secure transport protocol QUIC (e.g., used by HTTP/3) that processes PTB messages. In this paper, we present a novel parameterizable PTB detection algorithm for reliable transport protocols that does not depend on PTB messages. We further describe how to integrate our algorithm into QUIC, present results from an evaluation using the algorithm within a QUIC simulation model and, based on these results, suggest concrete parameter values.
Additive manufacturing (AM) has been growing continuously over the past 20 years, enabling unprecedented tailoring to the anatomy of each patient. In Europe, custom-made devices qualify for an exemption and pass a simplified approval process. New technologies, like AM, provoke questions about the adequacy of the current regulatory framework for custom-made devices. This article addresses the regulatory requirements for such devices in Europe and discusses the implications for AM. It concludes that the legal framework for custom-made devices entails uncertainties which need to be resolved to guide manufacturers through the regulatory requirements, highlighting the specific areas of focus for AM.
The use of computational modeling and simulation (CMS) as a tool for gaining insight into the technical performance and safety of medical devices has emerged continuously over the past years. However, to rely on information and decisions derived from model predictions, it is essential to establish model credibility for the specific context of use. Limited regulatory requirements and lack of consensus on the level of verification and validation activities required result in rare use of CMS as a source of evidence in the medical device approval process. The American Society of Mechanical Engineers (ASME) developed a risk-informed framework to establish appropriate credibility requirements of a computational model: the ASME V&V 40?2018 standard. This paper aims to outline the concepts of this standard and to demonstrate its application using an example from the orthotics field. The necessary steps to establish model credibility for a custom?made 3D printed wrist hand orthosis (WHO) are presented. It is shown that the credibility requirements of each verification and validation activity depend on model risk by applying two different contexts of use to the same computational model.
The paper describes the design, facilitation and outcomes of a series of workshops with faculty, staff and students from a teacher education program specialized in vocational education and training (VET). We analyze and reflect upon the facilitation techniques, discussion and participation results, and evaluation of the workshop series. Practitioners and researchers alike will find this article a valuable source for contemplating the effectiveness of design thinking, making and serious play in teacher education. While our case study is situated in the particular context of preparing future vocational teachers within the German VET system, the resulting concepts are applicable to other teacher education programs
Numerical investigation of a transonic dense gas flow over an idealized blade vane configuration
(2023)
CFD-SUPPORTED DATA REDUCTION OF HOT-WIRE ANEMOMETRY SIGNALS FOR COMPRESSIBLE ORGANIC VAPOR FLOWS
(2022)
Experimental and numerical study of transonic flow of an organic vapor past a circular cylinder
(2022)
Innovative Photoreaktoren in der Reaktionstechnik zur Wertstoffgewinnung und Abwasserbehandlung
(2016)
Novel Orange Emitting Phosphor (Tb1-xSmx)2Mo3O12 with Highly Efficient Tb3+ to Sm3+ Energy Transfer
(2014)
Solid State Synthesis and Reflection Spectra of Ce2Zr3(MoO4)9:Ti and Ce2Zr3(MoO4)9:Ti,Eu, FEMS
(2023)
On the Opportunities of the Use of Luminescence Standards for Cuvette Spectrometer, WODIL 2021
(2021)
Spectral properties and thermal quenching of Mn4+ luminescence in multicomponent garnet hosts
(2018)