Professur für Service Analytics

Neuigkeiten

Prof. Cleophas erhält mit Co-Autor*innen den Award for the Best EJOR Paper

16.07.2021

Professorin Catherine Cleophas erhält zusammen mit ihren Co-Autor*innen Caitlin Cottrill, Jan Fabian Ehmke und Kevin Tierney den

EURO Award for the Best EJOR Paper 2021

für die Review mit dem Titel "Collaborative urban transportation: Recent advances in theory and practice".

Link zum Papier: https://www.sciencedirect.com/science/article/pii/S0377221718303412

Neue Veröffentlichung im Journal of Revenue and Pricing Management

01.07.2021

Der Artikel "Nonparametric estimation of customer segments from censored sales panel data" von Johannes F. Jörg und Catherine Cleophas wurde im Journal of Revenue and Pricing Management veröffentlicht. 

DOI: https://doi.org/10.1057/s41272-021-00339-6

Abstract:
Specifically addressing different customer segments via revenue management or customer relationship management, lets firms optimize their market response. Identifying such segments requires analysing large amounts of transactional data. We present a nonparametric approach to estimate the number of customer segments from censored panel data. We evaluate several model selection criteria and imputation methods to compensate for censored observations under different demand scenarios. We measure estimation performance in a controlled environment via simulated data samples, benchmark it to common clustering indices and imputation methods, and analyse an empirical data sample to validate practical applicability.

Veröffentlichung im Journal Health Care Management Science

27.05.2021

Der Artikel "Patients, primary care, and policy: agent-based simulation modeling for health care decision support" von Martin Comis, Catherine Cleophas und Christina Büsing wurde im Journal Health Care Management Science veröffentlicht. 

DOI: https://doi.org/10.1007/s10729-021-09556-2

Abstract:
Primary care systems are a cornerstone of universally accessible health care. The planning, analysis, and adaptation of primary care systems is a highly non-trivial problem due to the systems’ inherent complexity, unforeseen future events, and scarcity of data. To support the search for solutions, this paper introduces the hybrid agent-based simulation model SiM-Care. SiM-Care models and tracks the micro-interactions of patients and primary care physicians on an individual level. At the same time, it models the progression of time via the discrete-event paradigm. Thereby, it enables modelers to analyze multiple key indicators such as patient waiting times and physician utilization to assess and compare primary care systems. Moreover, SiM-Care can evaluate changes in the infrastructure, patient behavior, and service design. To showcase SiM-Care and its validation through expert input and empirical data, we present a case study for a primary care system in Germany. Specifically, we study the immanent implications of demographic change on rural primary care and investigate the effects of an aging population and a decrease in the number of physicians, as well as their combined effects.

Neue Veröffentlichung im Journal of Revenue and Pricing Management

30.04.2021

Der Artikel "Clustering as an approach for creating data-driven perspectives on air travel itineraries" von Sebastian Vock, Laurie A. Garrow und Catherine Cleophas wurde im Journal of Revenue and Pricing Management veröffentlicht. 

DOI: https://doi.org/10.1057/s41272-021-00323-0

Abstract:
This paper proposes to create data-driven perspectives on airline ticketing data by defining groups of air travel itineraries independently of geographical or temporal attributes. We demonstrate the approach by analysing an extensive empirical data set featuring ticketing data from several carriers as collected by the Airlines Reporting Corporation. The analysis compares five cluster quality indicators to evaluate effects of pre-processing and clustering decisions. Benchmarking the results against a more traditional geographical grouping demonstrates the potential for data-driven analysis. Finally, we highlight ways in which the resulting cluster perspectives could support demand forecasting, performance evaluation, and analyst interventions.

Anmeldung zum Masterseminar im SoSe 2021 freigeschaltet

25.03.2021

Die Professur „Service Analytics“ bietet im Sommersemester 2021 wieder ein Masterseminar an. Informationen zum Thema und Ablauf des Seminars finden Sie in OLAT und im UnivIs.

Veröffentlichung im European Journal of Operational Research

08.01.2021

Der Artikel "Identifying and Responding to Outlier Demand in Revenue Management" von Nicola Rennie, Catherine Cleophas, Adam M. Sykulski und Florian Dost wurde im European Journal of Operational Research veröffentlicht. Das European Journal of Operational Research wird in der Liste VHB JOURQUAL mit dem Zeitschriftenrating “A” bewertet.

DOI: https://doi.org/10.1016/j.ejor.2021.01.002

Abstract:
Revenue management strongly relies on accurate forecasts. Thus, when extraordinary events cause outlier demand, revenue management systems need to recognise this and adapt both forecast and controls. Many passenger transport service providers, such as railways and airlines, control the sale of tickets through revenue management. State-of-the-art systems in these industries rely on analyst expertise to identify outlier demand both online (within the booking horizon) and offline (in hindsight). So far, little research focuses on automating and evaluating the detection of outlier demand in this context. To remedy this, we propose a novel approach, which detects outliers using functional data analysis in combination with time series extrapolation. We evaluate the approach in a simulation framework, which generates outliers by  arying the demand model. The results show that functional outlier detection yields better detection rates than alternative approaches for both online and offline analyses. Depending on the category of outliers, extrapolation further increases online detection performance. We also apply the procedure to a set of empirical data to demonstrate its practical implications. By evaluating the full feedback-driven system of forecast and optimisation, we generate insight on the asymmetric effects of positive and negative demand outliers. We show that identifying instances of outlier demand and adjusting the forecast in a timely fashion substantially increases revenue compared to what is earned when ignoring outliers.