Prof. Dr. Diane Ahrens

Forschung in den drei Zukunftsbereichen:

  • Smart Region/ “Digitales Dorf” (Leitung: Gudrun Fischer)
  • Business Data Analytics & Optimization (Leitung: Dr. Michael Scholz)
  • Applied Artificial Intelligence (Leitung: Prof. Dr. Benedikt Elser)

Beispielanwendungen:

Smart Region: Gleichwertige Lebensverhältnisse durch intelligente Nutzung digitaler Technologien und Lösungen im ländlichen Raum: digital unterstützte Mobilität, Co-Working Spaces, telemedizinanwendungen u.v.m. Betreuung von drei bayerischen Modelldörfern.

Business Data Analytics & Optimization: Geschäfts- und Sensordatenanalyse, Dispositions-, Materialfluss- und Layoutoptimierung von Fertigungen, Bestandsreduzierung und Parameteroptimierung von Bestellsystemen, Planungs- und Steuerungsalgorithmen für Industrie 4.0 Anwendungen, uvm.

Applied Artificial Intelligence: Big Data IT-Infrastruktur, maschinelle Lernverfahren für deskriptive Datenanalysen, prognosen und prädiktive Anwendungen, u.v.m.

Wissenschaftliche Leitung

Wissenschaftliche Leiterin

Grafenau

08552/975620-50


Bürozeiten

Dienstag bis Donnerstag, 8.00-16.00 Uhr, Freitag, 8.00-12.00 Uhr Terminanfragen über das TCG Sekretariat, Danyell Batey, Tel.: 08552/975620-51


Sprechzeiten

Terminanfragen über das TCG Sekretariat, Danyell Batey, Tel.: 08552/975620-51


Contribution
  • Sebastian Wilhelm
  • Dietmar Jakob
  • Jakob Kasbauer
  • Diane Ahrens
GeLaP: German Labeled Dataset for Power Consumption [Accepted for publication]
  • 2021
Due to the increasing spread of smart meters numerous researchers are currently working on disaggregating the power consumption data. This procedure is commonly known as Non-Intrusive Load Monitoring (NILM). However most approaches to energy disaggregation first require a labeled dataset to train these algorithms.In this paper we present a new labeled power consumption dataset that was collected in 20 private households in Germany between September 2019 and July 2020. For this purpose the total power consumption of each household was measured with a commercial available smart meter and the individual consumption data of 10 selected household appliances were collected.
Contribution
  • Sebastian Wilhelm
  • Dietmar Jakob
  • Jakob Kasbauer
  • Diane Ahrens
GeLaP: German Labeled Dataset for Power Consumption [Accepted for publication]
  • 2021
Due to the increasing spread of smart meters numerous researchers are currently working on disaggregating the power consumption data. This procedure is commonly known as Non-Intrusive Load Monitoring (NILM). However most approaches to energy disaggregation first require a labeled dataset to train these algorithms.In this paper we present a new labeled power consumption dataset that was collected in 20 private households in Germany between September 2019 and July 2020. For this purpose the total power consumption of each household was measured with a commercial available smart meter and the individual consumption data of 10 selected household appliances were collected.
JournalArticle
  • Mohammed Alnahhal
  • M. Tabash
  • Diane Ahrens
Optimal selection of third-party logistics providers using integer programming: a case study of a furniture company storage and distribution
  • 2021

DOI: 10.1007/s10479-021-04034-y

This paper investigates the selection of third-party logistics providers (3PLs) based on the best prices offered by them. The focus is on outbound logistics where 3PLs must have their own distribution centres for storage and picking activities. They must also have suitable trucks for distribution to different small-scale customers. The motivation for this paper is a case study from Germany in which a furniture company with hundreds of small customers in ten zones is seeking one or more 3PLs to do the distribution. A mathematical programming model was built based on integer programming where demand per order can be expressed using exponential distribution in each customer zone. The main contribution of this paper is that it finds the best 3PLs based on the different pricing methods of the various providers; this means including the location problem indirectly using the new integer programming model. The model takes into consideration three different methods of pricing based on the offers of five 3PLs. These different methods make it difficult for the decision makers to choose the best solution, especially if specific trends in demand are expected in the future for some customer zones. The results show that future increases in demand in terms of the number of orders or order size could affect the optimal solution. The best pricing method with the lowest variability in cost over time is selected.
Contribution
  • Diane Ahrens
Digitale Dörfer. Gleichwertige Lebensverhältnisse durch Digitalisierung im ländlichen Raum?
  • 2020
Book
  • S. Sczogiel
  • A. Busch
  • A. Göller
  • A. Gabber
  • B. Williger
  • S. Schmitt-Rüth
  • Diane Ahrens
  • Dietmar Jakob
  • Sebastian Wilhelm
Digital fit im Alter Handlungsempfehlung für Gemeinden zu Bildungsangeboten für Senioren (Hg.: Fraunhofer-Institut für Integrierte Schaltungen [IIS]; Technische Hochschule Deggendorf [THD])
  • 2020

DOI: 10.13140/RG.2.2.23245.05609

Ziel der Broschüre ist es, Gemeinden insbesondere im ländlichen Raum, über die Konzeption von Bildungsangeboten für ältere Menschen zu informieren und sie dazu zu befähigen, ähnliche Initiativen in ihren Gemeinden zu starten.
Lecture
  • Diane Ahrens
Räumliche Unabhängigkeit Dank Digitalisierung
  • 2020
Contribution
  • Lisa-Marie Hanninger
  • Jessica Laxa
  • Diane Ahrens
Rural Areas on Their Way to a Smart Village - Experiences from Living Labs in Bavaria
  • 2020
Contribution
  • Lisa-Marie Hanninger
  • Jessica Laxa
  • Diane Ahrens
Rural Areas on Their Way to a Smart Village - Experiences from Living Labs in Bavaria
  • 2020
Contribution
  • Sebastian Wilhelm
  • Dietmar Jakob
  • Diane Ahrens
Human Presence Detection by monitoring the indoor CO2 concentration
  • 2020

DOI: 10.1145/3404983.3409991

Presence detection systems are becoming more and more important and are used in smart home environments in the Ambient Assisted Living (AAL) domain or in surveillance technology. Common systems focus on using motion sensors or cameras which have only a limited viewing angle and therefore monitoring gaps can easily occur within a room. Humans produce carbon dioxide (CO2) through their respiration which is distributed in rooms. As a result if one (or more) persons are in a room a significant increase in CO2 concentration in the room can be noted. With this work we investigate an approach to detect the presence or absence of people indoors by monitoring the CO2 concentration in the ambient air.
JournalArticle
  • Ali Fallah-Tehrani
  • M. Strickert
  • Diane Ahrens
Class of Monotone Kernelized Classifiers on the basis of the Choquet Integral , vol37
  • 2020

DOI: 10.1111/exsy.12506

The key property of monotone classifiers is that increasing (decreasing) input values lead to increasing (decreasing) the output value. Preserving monotonicity for a classifier typically requires many constraints to be respected by modeling approaches such as artificial intelligence techniques. The type of constraints strongly depends on the modeling assumptions. Of course, for sophisticated models such conditions might be very complex. In this study we present a new family of kernels that we call it Choquet kernels. Henceforth it allows for employing popular kernel‐based methods such as support vector machines. Instead of a naïve approach with exponential computational complexity we propose an equivalent formulation with quadratic time in the number of attributes. Furthermore, since coefficients derived from kernel solutions are not necessarily monotone in the dual form, different approaches are proposed to monotonize coefficients. Finally experiments illustrate beneficial properties of the Choquet kernels.
JournalArticle
  • Ali Fallah-Tehrani
  • M. Strickert
  • Diane Ahrens
Class of Monotone Kernelized Classifiers on the basis of the Choquet Integral , vol37
  • 2020

DOI: 10.1111/exsy.12506

The key property of monotone classifiers is that increasing (decreasing) input values lead to increasing (decreasing) the output value. Preserving monotonicity for a classifier typically requires many constraints to be respected by modeling approaches such as artificial intelligence techniques. The type of constraints strongly depends on the modeling assumptions. Of course, for sophisticated models such conditions might be very complex. In this study we present a new family of kernels that we call it Choquet kernels. Henceforth it allows for employing popular kernel‐based methods such as support vector machines. Instead of a naïve approach with exponential computational complexity we propose an equivalent formulation with quadratic time in the number of attributes. Furthermore, since coefficients derived from kernel solutions are not necessarily monotone in the dual form, different approaches are proposed to monotonize coefficients. Finally experiments illustrate beneficial properties of the Choquet kernels.
Book
  • S. Sczogiel
  • A. Busch
  • A. Göller
  • A. Gabber
  • B. Williger
  • S. Schmitt-Rüth
  • Diane Ahrens
  • Dietmar Jakob
  • Sebastian Wilhelm
Digital fit im Alter Handlungsempfehlung für Gemeinden zu Bildungsangeboten für Senioren (Hg.: Fraunhofer-Institut für Integrierte Schaltungen [IIS]; Technische Hochschule Deggendorf [THD])
  • 2020

DOI: 10.13140/RG.2.2.23245.05609

Ziel der Broschüre ist es, Gemeinden insbesondere im ländlichen Raum, über die Konzeption von Bildungsangeboten für ältere Menschen zu informieren und sie dazu zu befähigen, ähnliche Initiativen in ihren Gemeinden zu starten.
Contribution
  • Diane Ahrens
Digitale Dörfer. Gleichwertige Lebensverhältnisse durch Digitalisierung im ländlichen Raum?
  • 2020
Lecture
  • Diane Ahrens
Räumliche Unabhängigkeit Dank Digitalisierung
  • 2020
Contribution
  • Sebastian Wilhelm
  • Dietmar Jakob
  • Diane Ahrens
Human Presence Detection by monitoring the indoor CO2 concentration
  • 2020

DOI: 10.1145/3404983.3409991

Presence detection systems are becoming more and more important and are used in smart home environments in the Ambient Assisted Living (AAL) domain or in surveillance technology. Common systems focus on using motion sensors or cameras which have only a limited viewing angle and therefore monitoring gaps can easily occur within a room. Humans produce carbon dioxide (CO2) through their respiration which is distributed in rooms. As a result if one (or more) persons are in a room a significant increase in CO2 concentration in the room can be noted. With this work we investigate an approach to detect the presence or absence of people indoors by monitoring the CO2 concentration in the ambient air.
Lecture
  • Diane Ahrens
Digitale Daseinsfürsorge in Stadt und Land
  • 2019
JournalArticle
  • Diane Ahrens
  • Sandra Gabert
Ländliche Wege in die digitale Zukunft Was ist bislang in den digitalen Dörfern passiert? , vol102
  • 2019
Lecture
  • Diane Ahrens
Zukunftsdörfer - Digitalisierung als Chance für den ländlichen Raum Keynote
  • 2019
Lecture
  • Diane Ahrens
Zukunftsdörfer: Digitalisierung im ländlichen Raum
  • 2019
Lecture
  • Diane Ahrens
Digitales Dorf - "Alles Smart!?"
  • 2019
JournalArticle
  • Diane Ahrens
  • Dietmar Jakob
Digitale Wege erkunden mit BLADL , vol102
  • 2019
Lecture
  • Diane Ahrens
Digitalisierung des ländlichen Raums
  • 2019
Lecture
  • Diane Ahrens
Zukunftsdörfer - Digitalisierung als Chance für den ländlichen Raum Keynote
  • 2019
JournalArticle
  • Diane Ahrens
  • Stefanie Seidenhofer
  • Gudrun Fischer
Die Technik macht es möglich
  • 2019
Lecture
  • Diane Ahrens
Digitale Daseinsfürsorge in Stadt und Land
  • 2019
Lecture
  • Diane Ahrens
Digitalisierung des ländlichen Raums
  • 2019
Contribution
  • Bernhard Bauer
  • Diane Ahrens
Datenbasierte Gäste- und Speiseprognosen in der Gemeinschaftsverpflegung
  • 2019

DOI: 10.1007/978-3-658-26954-8_11

Betriebsleiter in der Gemeinschaftsverpflegung stehen bei ihren Kalkulationen vor großen Herausforderungen. Sowohl externe Einflüsse als auch der Speiseplan selbst beeinflussen die Zahl der Essensteilnehmer. Die Verkaufsmengen der einzelnen Speisen weisen einen hohen Zusammenhang mit der Zusammenstellung des Speiseplans auf. Die Kalkulation der Einkaufs‐ und Produziermengen basiert in den meisten Gastronomien auf der Expertise und den Erfahrungswerten der Betriebsleiter und Küchenchefs. Ziel dieses Beitrags ist die Entwicklung von Prognosealgorithmen für die Gemeinschaftsverpflegung, um die Kalkulationen von Lebensmitteleinkäufen zu erleichtern und somit den Verlust von Lebensmitteln zu verringern. Dazu werden die Daten aus drei Pilotunternehmen mithilfe von statistischen Datenanalysemethoden untersucht. Es werden Prognosemodelle entwickelt, die die zukünftigen Essensteilnehmerzahlen sowie die Anzahl an benötigten Portionen pro Gericht vorhersagen und damit die Kalkulationen für Küchenleiter erleichtern sollen. Eine Verallgemeinerung der Modelle sichert die Übertragbarkeit auf andere Betriebsgastronomien.
Lecture
  • Diane Ahrens
Smart Villages in Bavaria: A Living Lab Approach to Prevent Urbanization
  • 2019
Lecture
  • Diane Ahrens
  • Bernhard Bauer
Prognose und Monitoring in der Gemeinschaftsverpflegung - Optimierter Wareneinsatz durch Big Data
  • 2019
JournalArticle
  • Diane Ahrens
  • Stefanie Seidenhofer
  • Gudrun Fischer
Die Technik macht es möglich
  • 2019
Book
  • Akademie für Politische Bildung Tutzing
  • Bayerischer Landtag
  • Diane Ahrens
Akademiegespräche im Bayerischen Landtag Diane Ahrens: Zukunftsdörfer - Digitalisierung als Chance für den ländlichen Raum. Veranstaltung vom 9. April 2019
  • 2019
Book
  • Akademie für Politische Bildung Tutzing
  • Bayerischer Landtag
  • Diane Ahrens
Akademiegespräche im Bayerischen Landtag Diane Ahrens: Zukunftsdörfer - Digitalisierung als Chance für den ländlichen Raum. Veranstaltung vom 9. April 2019
  • 2019
Lecture
  • Diane Ahrens
Digitales Dorf - "Alles Smart!?"
  • 2019
Lecture
  • Diane Ahrens
  • Bernhard Bauer
Prognose und Monitoring in der Gemeinschaftsverpflegung - Optimierter Wareneinsatz durch Big Data
  • 2019
Lecture
  • Diane Ahrens
Zukunftsdörfer: Digitalisierung im ländlichen Raum
  • 2019
JournalArticle
  • Diane Ahrens
  • Dietmar Jakob
Digitale Wege erkunden mit BLADL , vol102
  • 2019
Contribution
  • Bernhard Bauer
  • Diane Ahrens
Datenbasierte Gäste- und Speiseprognosen in der Gemeinschaftsverpflegung
  • 2019

DOI: 10.1007/978-3-658-26954-8_11

Betriebsleiter in der Gemeinschaftsverpflegung stehen bei ihren Kalkulationen vor großen Herausforderungen. Sowohl externe Einflüsse als auch der Speiseplan selbst beeinflussen die Zahl der Essensteilnehmer. Die Verkaufsmengen der einzelnen Speisen weisen einen hohen Zusammenhang mit der Zusammenstellung des Speiseplans auf. Die Kalkulation der Einkaufs‐ und Produziermengen basiert in den meisten Gastronomien auf der Expertise und den Erfahrungswerten der Betriebsleiter und Küchenchefs. Ziel dieses Beitrags ist die Entwicklung von Prognosealgorithmen für die Gemeinschaftsverpflegung, um die Kalkulationen von Lebensmitteleinkäufen zu erleichtern und somit den Verlust von Lebensmitteln zu verringern. Dazu werden die Daten aus drei Pilotunternehmen mithilfe von statistischen Datenanalysemethoden untersucht. Es werden Prognosemodelle entwickelt, die die zukünftigen Essensteilnehmerzahlen sowie die Anzahl an benötigten Portionen pro Gericht vorhersagen und damit die Kalkulationen für Küchenleiter erleichtern sollen. Eine Verallgemeinerung der Modelle sichert die Übertragbarkeit auf andere Betriebsgastronomien.
Lecture
  • Diane Ahrens
Smart Villages in Bavaria: A Living Lab Approach to Prevent Urbanization
  • 2019
JournalArticle
  • Diane Ahrens
  • Sandra Gabert
Ländliche Wege in die digitale Zukunft Was ist bislang in den digitalen Dörfern passiert? , vol102
  • 2019
Lecture
  • Diane Ahrens
Digitales Dorf
  • 2018
Lecture
  • Diane Ahrens
Digitale Hörnerdörfer
  • 2018
Lecture
  • Diane Ahrens
Digitale Hörnerdörfer
  • 2018
JournalArticle
  • Diane Ahrens
Frauenau und Spiegelau werden digital , vol71
  • 2018
Lecture
  • Diane Ahrens
Digitales Dorf
  • 2018
Lecture
  • Diane Ahrens
Anhörung als Sachverständige im Bayerischen Landtag zum Thema "Sicherung der wohnortnahen Versorgung in der Kommune"
  • 2018
JournalArticle
  • Mohammed Alnahhal
  • Diane Ahrens
A Simulation-Based System for Calculating Optimal Numbers of Forklift Drivers in Industrial Plants , vol4
  • 2018

DOI: 10.25929/bjas.v4i1.53

Dieser Artikel beschreibt eine Optimierungsmethode für ein Materialtransportsystem von Gabelstaplern mittels Warteschlangentheorie und Simulation. Ziel ist es, verschiedene Arten von Verschwendung bei den Kapazitätskosten, verspäteten Arbeitsaufträgen und Transportkosten zu reduzieren. Es wird eine gewisse ITInfrastruktur angenommen, wie etwa die Verwendung von Monitoren, um die aktuellen Arbeitsaufträge von verschiedenen Arbeitsplätzen anzuzeigen. Mathematische Gleichungen werden benutzt, um anfängliche obere und untere Grenzen für die benötigten Kapazitätsniveaus zu finden. Danach wird eine Simulation für verschiedene Kapazitätsniveaus innerhalb des Bereichs der theoretischen Ergebnisse durchgeführt, um die genau benötigte Mannzeit für verschiedene Jobsequenzierungsstrategien zu finden. Mit Hilfe der Statistiksoftware R wird ein Tool erstellt, welches Unternehmen für verschiedene Parameter Ergebnisse liefert. Diese Ergebnisse zeigen die Auswirkungen der Verwendung von Batching, unter Berücksichtigung der Begrenzung des Zeilenseitenraums und der Reduzierung der Leerfahrtstrategie für Leistungsmessungen. Die Strategie, das Leerfahren zu reduzieren, indem nach dem nächsten Arbeitsplatz gesucht wird, der einen Auftrag benötigt, ist nicht so effizient, da es die benötigte Kapazität erhöht. Dies liegt daran, dass es die Variabilität der Wartezeit vergrößert und somit den Prozentsatz der verspäteten Bestellungen steigert.
Lecture
  • Diane Ahrens
Digitales Dorf - Gemeinsam digitale Zukunft schaffen
  • 2018
Lecture
  • Diane Ahrens
Digitalisierung als Chance für ländliche Gemeinden
  • 2018
Lecture
  • Diane Ahrens
Anhörung als Sachverständige im Bayerischen Landtag zum Thema "Sicherung der wohnortnahen Versorgung in der Kommune"
  • 2018
Lecture
  • Diane Ahrens
Mega-Trend Digitalisierung
  • 2018
Contribution
  • Ali Fallah-Tehrani
  • Diane Ahrens
Enhanced Predictive Models for Purchasing in the Fashion Field by Applying Regression Trees Equipped with Ordinal Logistic Regression
  • 2018

DOI: 10.1007/978-981-13-0080-6_3

Identifying the products which are highly sold in the fashion apparel industry is one of the challenging tasks, which leads to reduce the write-off and increase the revenue. Assuming three classes as substantial, middle, and inconsiderable, the forecasting problem comes down to a classification problem, where the task is to predict the class of a product. In this research, we present a probabilistic approach to identify the class of fashion products in terms of sale. In previous work, we showed that a combination of kernel machines with a probabilistic approach may empower the performance of kernel machines. However, a well-known drawback of kernel machines is its non-interpretability. The interpretability is one of the most important features from an user point of view; essentially in the fashion field, decision makers require to understand and interpret the model for a more convenient adaptation. Since regression trees can be formulated through rules, this makes possible to comprehend the model. Nevertheless, a drawback of decision trees is the sensibility to input space, which may cause very enormous deviations in terms of prediction. To reduce this effect on forecast, we propose a new model equipped with ordinal logistic regression. Finally to verify the proposed approach, we conducted several experiments on a real data extracted from an apparel retailer in Germany.
Lecture
  • Diane Ahrens
Digitalisierung als Chance für ländliche Gemeinden
  • 2018
Contribution
  • Ali Fallah-Tehrani
  • Diane Ahrens
Enhanced Predictive Models for Purchasing in the Fashion Field by Applying Regression Trees Equipped with Ordinal Logistic Regression
  • 2018

DOI: 10.1007/978-981-13-0080-6_3

Identifying the products which are highly sold in the fashion apparel industry is one of the challenging tasks, which leads to reduce the write-off and increase the revenue. Assuming three classes as substantial, middle, and inconsiderable, the forecasting problem comes down to a classification problem, where the task is to predict the class of a product. In this research, we present a probabilistic approach to identify the class of fashion products in terms of sale. In previous work, we showed that a combination of kernel machines with a probabilistic approach may empower the performance of kernel machines. However, a well-known drawback of kernel machines is its non-interpretability. The interpretability is one of the most important features from an user point of view; essentially in the fashion field, decision makers require to understand and interpret the model for a more convenient adaptation. Since regression trees can be formulated through rules, this makes possible to comprehend the model. Nevertheless, a drawback of decision trees is the sensibility to input space, which may cause very enormous deviations in terms of prediction. To reduce this effect on forecast, we propose a new model equipped with ordinal logistic regression. Finally to verify the proposed approach, we conducted several experiments on a real data extracted from an apparel retailer in Germany.
Lecture
  • Diane Ahrens
Digitales Dorf
  • 2018
Lecture
  • Diane Ahrens
Digitale Hörnerdörfer
  • 2018
Lecture
  • Diane Ahrens
Mega-Trend Digitalisierung
  • 2018
Lecture
  • Diane Ahrens
Digitale Hörnerdörfer
  • 2018
Lecture
  • Diane Ahrens
Digitalisierung als Chance für ländliche Gemeinden
  • 2018
JournalArticle
  • Mohammed Alnahhal
  • Diane Ahrens
A Simulation-Based System for Calculating Optimal Numbers of Forklift Drivers in Industrial Plants , vol4
  • 2018

DOI: 10.25929/bjas.v4i1.53

Dieser Artikel beschreibt eine Optimierungsmethode für ein Materialtransportsystem von Gabelstaplern mittels Warteschlangentheorie und Simulation. Ziel ist es, verschiedene Arten von Verschwendung bei den Kapazitätskosten, verspäteten Arbeitsaufträgen und Transportkosten zu reduzieren. Es wird eine gewisse ITInfrastruktur angenommen, wie etwa die Verwendung von Monitoren, um die aktuellen Arbeitsaufträge von verschiedenen Arbeitsplätzen anzuzeigen. Mathematische Gleichungen werden benutzt, um anfängliche obere und untere Grenzen für die benötigten Kapazitätsniveaus zu finden. Danach wird eine Simulation für verschiedene Kapazitätsniveaus innerhalb des Bereichs der theoretischen Ergebnisse durchgeführt, um die genau benötigte Mannzeit für verschiedene Jobsequenzierungsstrategien zu finden. Mit Hilfe der Statistiksoftware R wird ein Tool erstellt, welches Unternehmen für verschiedene Parameter Ergebnisse liefert. Diese Ergebnisse zeigen die Auswirkungen der Verwendung von Batching, unter Berücksichtigung der Begrenzung des Zeilenseitenraums und der Reduzierung der Leerfahrtstrategie für Leistungsmessungen. Die Strategie, das Leerfahren zu reduzieren, indem nach dem nächsten Arbeitsplatz gesucht wird, der einen Auftrag benötigt, ist nicht so effizient, da es die benötigte Kapazität erhöht. Dies liegt daran, dass es die Variabilität der Wartezeit vergrößert und somit den Prozentsatz der verspäteten Bestellungen steigert.
Lecture
  • Diane Ahrens
Digitales Dorf
  • 2018
Lecture
  • Diane Ahrens
Digitales Dorf - Gemeinsam digitale Zukunft schaffen
  • 2018
Lecture
  • Diane Ahrens
Digitalisierung als Chance für ländliche Gemeinden
  • 2018
JournalArticle
  • Diane Ahrens
Frauenau und Spiegelau werden digital , vol71
  • 2018
JournalArticle
  • Ali Fallah-Tehrani
  • Diane Ahrens
Modeling Label Dependence for Multi-Label Classification Using the Choquistic Regression , vol92
  • 2017

DOI: 10.1016/j.patrec.2017.04.018

While an incorrect identification of underlying dependency in data can lead to a flawed conclusion, recognizing legitimate dependency allows for the opportunity to adapt a model in a correct manner. In this regard, modeling the inter-dependencies in multi-label classification (multi target prediction) is one of the challenging tasks from a machine learning point of view. While common approaches seek to exploit so-called correlated information from labels, this can be improved by assuming the interactions between labels. A well-known tool to model the interaction between attributes is the Choquet integral; it enables one to model non-linear dependencies between attributes. Beyond identifying proper prior knowledge in data (if such knowledge exists), establishing suitable models that are in agreement with prior knowledge is not always a trivial task. In this paper, we propose a first step towards modeling label dependencies for multi-target classifications in terms of positive and negative interactions. In the experimental, we demonstrate real gains by applying this approach.
JournalArticle
  • Ali Fallah-Tehrani
  • Diane Ahrens
Modified Sequential k‐means Clustering by Utilizing Response: A Case Study for Fashion Products , vol34
  • 2017

DOI: 10.1111/exsy.12226

Modified sequential k‐means clustering concerns a k‐means clustering problem in which the clustering machine utilizes output similarity in addition. While conventional clustering methods commonly recognize similar instances at features‐level modified sequential clustering takes advantage of response, too. To this end, the approach we pursue is to enhance the quality of clustering by using some proper information. The information enables the clustering machine to detect more patterns and dependencies that may be relevant. This allows one to determine, for instance, which fashion products exhibit similar behaviour in terms of sales. Unfortunately, conventional clustering methods cannot tackle such cases, because they handle attributes solely at the feature level without considering any response. In this study, we introduce a novel approach underlying minimum conditional entropy clustering and show its advantages in terms of data analytics. In particular, we achieve this by modifying the conventional sequential k‐means algorithm. This modified clustering approach has the ability to reflect the response effect in a consistent manner. To verify the feasibility and the performance of this approach, we conducted several experiments based on real data from the apparel industry.
Lecture
  • Diane Ahrens
Digital Village - A Bavarian Initiative
  • 2017
JournalArticle
  • Ali Fallah-Tehrani
  • Diane Ahrens
Modeling Label Dependence for Multi-Label Classification Using the Choquistic Regression , vol92
  • 2017

DOI: 10.1016/j.patrec.2017.04.018

While an incorrect identification of underlying dependency in data can lead to a flawed conclusion, recognizing legitimate dependency allows for the opportunity to adapt a model in a correct manner. In this regard, modeling the inter-dependencies in multi-label classification (multi target prediction) is one of the challenging tasks from a machine learning point of view. While common approaches seek to exploit so-called correlated information from labels, this can be improved by assuming the interactions between labels. A well-known tool to model the interaction between attributes is the Choquet integral; it enables one to model non-linear dependencies between attributes. Beyond identifying proper prior knowledge in data (if such knowledge exists), establishing suitable models that are in agreement with prior knowledge is not always a trivial task. In this paper, we propose a first step towards modeling label dependencies for multi-target classifications in terms of positive and negative interactions. In the experimental, we demonstrate real gains by applying this approach.
Lecture
  • Diane Ahrens
Digitales Dorf - Von der Vision zur Modellregion
  • 2017
Lecture
  • Diane Ahrens
Digitales Dorf - Von der Vision zur Modellregion
  • 2017
Lecture
  • Diane Ahrens
Digitales Dorf - Von der Vision zur Modellregion
  • 2017
Contribution
  • Nari Arunraj
  • Diane Ahrens
Improving food supply chain using hybrid semiparametric regression model
  • 2017
Lecture
  • Diane Ahrens
Digital Village - A Bavarian Initiative
  • 2017
Lecture
  • Diane Ahrens
Prognosen im Lebensmittelkonsum: Weniger Lebensmittelverluste durch Optimierung von Prognosen und Disposition
  • 2017
JournalArticle
  • Ali Fallah-Tehrani
  • Diane Ahrens
Modified Sequential k‐means Clustering by Utilizing Response: A Case Study for Fashion Products , vol34
  • 2017

DOI: 10.1111/exsy.12226

Modified sequential k‐means clustering concerns a k‐means clustering problem in which the clustering machine utilizes output similarity in addition. While conventional clustering methods commonly recognize similar instances at features‐level modified sequential clustering takes advantage of response, too. To this end, the approach we pursue is to enhance the quality of clustering by using some proper information. The information enables the clustering machine to detect more patterns and dependencies that may be relevant. This allows one to determine, for instance, which fashion products exhibit similar behaviour in terms of sales. Unfortunately, conventional clustering methods cannot tackle such cases, because they handle attributes solely at the feature level without considering any response. In this study, we introduce a novel approach underlying minimum conditional entropy clustering and show its advantages in terms of data analytics. In particular, we achieve this by modifying the conventional sequential k‐means algorithm. This modified clustering approach has the ability to reflect the response effect in a consistent manner. To verify the feasibility and the performance of this approach, we conducted several experiments based on real data from the apparel industry.
Lecture
  • Diane Ahrens
Digitalisierung als Chance für ländliche Gemeinden
  • 2017
Contribution
  • Nari Arunraj
  • Diane Ahrens
Improving food supply chain using hybrid semiparametric regression model
  • 2017
Lecture
  • Diane Ahrens
Digitalisierung als Chance für ländliche Gemeinden
  • 2017
Lecture
  • Diane Ahrens
Digitalisierung als Chance für ländliche Gemeinden
  • 2017
Lecture
  • Diane Ahrens
Prognosen im Lebensmittelkonsum: Weniger Lebensmittelverluste durch Optimierung von Prognosen und Disposition
  • 2017
Lecture
  • Diane Ahrens
Digitalisierung als Chance für ländliche Gemeinden
  • 2017
Lecture
  • Diane Ahrens
Märkte im Wandel - Anforderungen an die Logistik
  • 2017
Lecture
  • Diane Ahrens
Märkte im Wandel - Anforderungen an die Logistik
  • 2017
Lecture
  • Diane Ahrens
Digitales Dorf - Von der Vision zur Modellregion
  • 2017
Contribution
  • Ali Fallah-Tehrani
  • Diane Ahrens
Improved Forecasting and Purchasing of Fashion Products based on the Use of Big Data Techniques
  • 2016

DOI: 10.1007/978-3-658-08809-5_13

Ordering proper amount of products, taking into account the demand of market, in fashion retail industries is one of the core challenges. Essentially due to the fact that the ordering is typically performed once in the each season, it is absolutely required to carry out precise orders. To make a precise ordering as well as to prevent overstocks and stock-out, there is a need for reliable forecasting methods. A reliable forecasting requires to consider proper predictive models which can consider all deciding factors. Specifically in the case of fashion forecasting since each product is associated with several factors, e.g. price, style, color and even human factors, learn a suitable predictive model is not an easy task. In fact, the challenge here boils down to learn a powerful model, which can cover all these information. To this end, big data techniques, namely data mining and machine learning methods serve the ability to accomplish the challenge. In this paper, we exploit unsupervised learning methods for a goal fitting the data, particularly w.r.t simple models although with higher gain. In essence, our innovative model is able to modify simple regression model, and hence, provide more promising results. In this regard, we apply big data analyses and techniques specifically in fashion field to analyze and make the salesprediction.
JournalArticle
  • Ali Fallah-Tehrani
  • Diane Ahrens
Supervised Regression Clustering: A Case Study for Fashion Products , vol3
  • 2016

DOI: 10.4018/IJBAN.2016100102

Clustering techniques typically group similar instances underlying individual attributes by supposing that similar instances have similar attributes characteristic. On contrary, clustering similar instances given a specific behavior is framed through supervised learning. For instance, which fashion products have similar behavior in term of sales. Unfortunately, conventional clustering methods cannot tackle this case, since they handle attributes by a same manner. In fact, conventional clustering approaches do not consider any response, and moreover they assume attributes act by the same importance. However, clustering instances with respect to responses leads to a better data analytics. In this research, the authors introduce an approach for the goal supervised clustering and show its advantage in terms of data analytics as well as prediction. To verify the feasibility and the performance of this approach the authors conducted several experiments on a real dataset derived from an apparel industry.
Lecture
  • Diane Ahrens
Intelligente Warenwirtschaftssysteme mit praktischen Umsetzungsbeispielen im Handel
  • 2016
JournalArticle
  • Nari Arunraj
  • Diane Ahrens
  • Michael Fernandes
Application of SARIMAX model to forecast daily sales in retail industry , vol7
  • 2016

DOI: 10.4018/IJORIS.2016040101

Abstract During retail stage of food supply chain (FSC), food waste and stock-outs occur mainly due to inaccurate sales forecasting which leads to inappropriate ordering of products. The daily demand for a fresh food product is affected by external factors, such as seasonality, price reductions and holidays. In order to overcome this complexity and inaccuracy, the sales forecasting should try to consider all the possible demand influencing factors. The objective of this study is to develop a Seasonal Autoregressive Integrated Moving Average with external variables (SARIMAX) model which tries to account all the effects due to the demand influencing factors, to forecast the daily sales of perishable foods in a retail store. With respect to performance measures, it is found that the proposed SARIMAX model improves the traditional Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Article Preview 1. Introduction Discount retail stores have been a noticeable feature of German retail market since the 1980s. In particular, the growth in number of discount retail stores have significantly increased after reunification of Germany. Recently, there is a growing trend of increasing varieties of fruits and vegetables with year-around availability across all the German discount retail outlets rather than just in their traditional growing season. In order to attract customers and remain competitive in the market, the fruits and vegetables are exported from foreign countries and stocked for longer periods. Particularly, increase in number of retail stores, availability of varieties of fruits and vegetables (in stock) with short shelf-lives, frequent price variations, and different storage conditions increase the complexity and results in huge amount of food waste. In Germany, the retail sector produces the food waste of around 0.5 million tons per year (Kranert et al., 2012). Although the retail sector contributes only 5% of the total food waste in food supply chain, mostly they are avoidable food waste (wasting food which is fit for consumption). The quantity of food waste that occurs in the home (61%) is partially due to the management decisions in the retail sector (e.g. frequent promotions) that stimulate the consumer’s eagerness to purchase, and distract them to equate their demand with the purchase (Arunraj et al., 2014; Gooch et al., 2010). Hence, the proper decision making in the retail sector can help the suppliers and consumers to avoid the food waste. The role of sales forecasting in reducing the food waste in retail stores is a significant topic of discussion in the recent food waste related studies (Mena et al., 2011; Mena et al., 2014). According to Mena et al. (2011) and Stenmarck et al. (2011), the improvement of forecast accuracy is one of the essential remedial measures to reduce the food waste in the retail sector of food supply chain.
JournalArticle
  • Diane Ahrens
  • I. Häberle
  • P. Muranyi
Zu große Energiemengen landen im Müll. Energieverluste durch Lebensmittelverschwendung - Experten zeigen Einsparpotentiale auf , vol96
  • 2016
JournalArticle
  • Diane Ahrens
  • I. Häberle
  • P. Muranyi
Zu große Energiemengen landen im Müll. Energieverluste durch Lebensmittelverschwendung - Experten zeigen Einsparpotentiale auf , vol96
  • 2016
JournalArticle
  • Ali Fallah-Tehrani
  • Diane Ahrens
Enhanced predictive models for purchasing in the fashion field by using kernel machine regression equipped with ordinal logistic regression , vol32
  • 2016
Identifying the products which are highly sold in the fashion apparel industry is one of the challenging tasks, which leads to reduce the write off and increases the revenue. In fact, beyond of sales forecasting in general a crucial question remains whether a product may sell well or not. Assuming three classes as substantial, middle and inconsiderable, the forecasting problem comes down to a classification problem, where the task is to predict the class of a product. In this research, we present a probabilistic approach to identify the class of fashion products in terms of sale. Thereafter, we combine kernel machines with a probabilistic approach to empower the performance of kernel machines and eventually to make use of it to predicting the number of sales. The proposed approach is more robust to outliers (in the case of highly sold products) and in addition uses prior knowledge, hence it serves more reliable results. In order to verify the proposed approach, we conducted several experiments on a real data extracted from an apparel retailer in Germany.
Lecture
  • Diane Ahrens
Impulses for Economics and Region by Means of Decentralization of Research
  • 2016
JournalArticle
  • Ali Fallah-Tehrani
  • Diane Ahrens
Supervised Regression Clustering: A Case Study for Fashion Products , vol3
  • 2016

DOI: 10.4018/IJBAN.2016100102

Clustering techniques typically group similar instances underlying individual attributes by supposing that similar instances have similar attributes characteristic. On contrary, clustering similar instances given a specific behavior is framed through supervised learning. For instance, which fashion products have similar behavior in term of sales. Unfortunately, conventional clustering methods cannot tackle this case, since they handle attributes by a same manner. In fact, conventional clustering approaches do not consider any response, and moreover they assume attributes act by the same importance. However, clustering instances with respect to responses leads to a better data analytics. In this research, the authors introduce an approach for the goal supervised clustering and show its advantage in terms of data analytics as well as prediction. To verify the feasibility and the performance of this approach the authors conducted several experiments on a real dataset derived from an apparel industry.
JournalArticle
  • Nari Arunraj
  • Diane Ahrens
  • Michael Fernandes
Application of SARIMAX model to forecast daily sales in retail industry , vol7
  • 2016

DOI: 10.4018/IJORIS.2016040101

Abstract During retail stage of food supply chain (FSC), food waste and stock-outs occur mainly due to inaccurate sales forecasting which leads to inappropriate ordering of products. The daily demand for a fresh food product is affected by external factors, such as seasonality, price reductions and holidays. In order to overcome this complexity and inaccuracy, the sales forecasting should try to consider all the possible demand influencing factors. The objective of this study is to develop a Seasonal Autoregressive Integrated Moving Average with external variables (SARIMAX) model which tries to account all the effects due to the demand influencing factors, to forecast the daily sales of perishable foods in a retail store. With respect to performance measures, it is found that the proposed SARIMAX model improves the traditional Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Article Preview 1. Introduction Discount retail stores have been a noticeable feature of German retail market since the 1980s. In particular, the growth in number of discount retail stores have significantly increased after reunification of Germany. Recently, there is a growing trend of increasing varieties of fruits and vegetables with year-around availability across all the German discount retail outlets rather than just in their traditional growing season. In order to attract customers and remain competitive in the market, the fruits and vegetables are exported from foreign countries and stocked for longer periods. Particularly, increase in number of retail stores, availability of varieties of fruits and vegetables (in stock) with short shelf-lives, frequent price variations, and different storage conditions increase the complexity and results in huge amount of food waste. In Germany, the retail sector produces the food waste of around 0.5 million tons per year (Kranert et al., 2012). Although the retail sector contributes only 5% of the total food waste in food supply chain, mostly they are avoidable food waste (wasting food which is fit for consumption). The quantity of food waste that occurs in the home (61%) is partially due to the management decisions in the retail sector (e.g. frequent promotions) that stimulate the consumer’s eagerness to purchase, and distract them to equate their demand with the purchase (Arunraj et al., 2014; Gooch et al., 2010). Hence, the proper decision making in the retail sector can help the suppliers and consumers to avoid the food waste. The role of sales forecasting in reducing the food waste in retail stores is a significant topic of discussion in the recent food waste related studies (Mena et al., 2011; Mena et al., 2014). According to Mena et al. (2011) and Stenmarck et al. (2011), the improvement of forecast accuracy is one of the essential remedial measures to reduce the food waste in the retail sector of food supply chain.
Lecture
  • Diane Ahrens
Impulses for Economics and Region by Means of Decentralization of Research
  • 2016
JournalArticle
  • Ali Fallah-Tehrani
  • Diane Ahrens
Enhanced predictive models for purchasing in the fashion field by using kernel machine regression equipped with ordinal logistic regression , vol32
  • 2016
Identifying the products which are highly sold in the fashion apparel industry is one of the challenging tasks, which leads to reduce the write off and increases the revenue. In fact, beyond of sales forecasting in general a crucial question remains whether a product may sell well or not. Assuming three classes as substantial, middle and inconsiderable, the forecasting problem comes down to a classification problem, where the task is to predict the class of a product. In this research, we present a probabilistic approach to identify the class of fashion products in terms of sale. Thereafter, we combine kernel machines with a probabilistic approach to empower the performance of kernel machines and eventually to make use of it to predicting the number of sales. The proposed approach is more robust to outliers (in the case of highly sold products) and in addition uses prior knowledge, hence it serves more reliable results. In order to verify the proposed approach, we conducted several experiments on a real data extracted from an apparel retailer in Germany.
JournalArticle
  • Nari Arunraj
  • Diane Ahrens
Estimation of Non-Catastrophic Weather Impacts for Retail Industry , vol44
  • 2016
Purpose Weather is often referred as an uncontrollable factor, which influences customer’s buying decisions and causes the demand to move in any direction. Such a risk usually leads to loss to industries. However, only few research studies about weather and retail shopping are available in literature. This study aims at developing a model and to analyse the relationship between weather and retail shopping behavior (i.e., store traffic and sales). Design/methodology/approach. The data set for this research study is obtained from two food retail stores and a fashion retail store located in Lower Bavaria, Germany. All these three retail stores are in same geographical location. The weather data set was provided by a German weather service agency and is from a weather station nearer to the retail stores under study. The analysis for the study was drawn using multiple linear regression with autoregressive elements (MLR-AR). The estimated coefficients of weather variables using MLR-AR model represent corresponding weather impacts on the store traffic and the sales. Findings The snowfall has a significant effect on the store traffic and the sales in both food and fashion retail stores. In food retail store, the risk due to snowfall varies depending on the location of stores. There are also significant lagging effects of snowfall in the fashion retail store. However, the rainfall has a significant effect only on the store traffic in the food retail stores. In addition to these effects, the sales in the fashion retail store are highly affected by the temperature deviation. Research limitations/implications Limitations in availability of data for the weather variables and other demand influencing factors (e.g. promotion, tourism, online shopping, demography of customers etc.) may reduce efficiency of the proposed MLR-AR model. In spite of these limitations, this study can be able to quantify the effects of weather variables on the store traffic and the sales Originality/value. This study contributes to the field of retail distribution by providing significant evidence of relationship between weather and retail business. Unlike previous studies, the proposed model tries to consider autocorrelation property, main and interaction effects between weather variables, temperature deviation and lagging effects of snowfall on the store traffic or the sales. The estimated weather impacts from this model can act as a reliable tool for retailers to explain the importance of different non-catastrophic weather events.
Lecture
  • Diane Ahrens
Intelligente Warenwirtschaftssysteme mit praktischen Umsetzungsbeispielen im Handel
  • 2016
JournalArticle
  • Nari Arunraj
  • Diane Ahrens
Estimation of Non-Catastrophic Weather Impacts for Retail Industry , vol44
  • 2016
Purpose Weather is often referred as an uncontrollable factor, which influences customer’s buying decisions and causes the demand to move in any direction. Such a risk usually leads to loss to industries. However, only few research studies about weather and retail shopping are available in literature. This study aims at developing a model and to analyse the relationship between weather and retail shopping behavior (i.e., store traffic and sales). Design/methodology/approach. The data set for this research study is obtained from two food retail stores and a fashion retail store located in Lower Bavaria, Germany. All these three retail stores are in same geographical location. The weather data set was provided by a German weather service agency and is from a weather station nearer to the retail stores under study. The analysis for the study was drawn using multiple linear regression with autoregressive elements (MLR-AR). The estimated coefficients of weather variables using MLR-AR model represent corresponding weather impacts on the store traffic and the sales. Findings The snowfall has a significant effect on the store traffic and the sales in both food and fashion retail stores. In food retail store, the risk due to snowfall varies depending on the location of stores. There are also significant lagging effects of snowfall in the fashion retail store. However, the rainfall has a significant effect only on the store traffic in the food retail stores. In addition to these effects, the sales in the fashion retail store are highly affected by the temperature deviation. Research limitations/implications Limitations in availability of data for the weather variables and other demand influencing factors (e.g. promotion, tourism, online shopping, demography of customers etc.) may reduce efficiency of the proposed MLR-AR model. In spite of these limitations, this study can be able to quantify the effects of weather variables on the store traffic and the sales Originality/value. This study contributes to the field of retail distribution by providing significant evidence of relationship between weather and retail business. Unlike previous studies, the proposed model tries to consider autocorrelation property, main and interaction effects between weather variables, temperature deviation and lagging effects of snowfall on the store traffic or the sales. The estimated weather impacts from this model can act as a reliable tool for retailers to explain the importance of different non-catastrophic weather events.
Contribution
  • Ali Fallah-Tehrani
  • Diane Ahrens
Improved Forecasting and Purchasing of Fashion Products based on the Use of Big Data Techniques
  • 2016

DOI: 10.1007/978-3-658-08809-5_13

Ordering proper amount of products, taking into account the demand of market, in fashion retail industries is one of the core challenges. Essentially due to the fact that the ordering is typically performed once in the each season, it is absolutely required to carry out precise orders. To make a precise ordering as well as to prevent overstocks and stock-out, there is a need for reliable forecasting methods. A reliable forecasting requires to consider proper predictive models which can consider all deciding factors. Specifically in the case of fashion forecasting since each product is associated with several factors, e.g. price, style, color and even human factors, learn a suitable predictive model is not an easy task. In fact, the challenge here boils down to learn a powerful model, which can cover all these information. To this end, big data techniques, namely data mining and machine learning methods serve the ability to accomplish the challenge. In this paper, we exploit unsupervised learning methods for a goal fitting the data, particularly w.r.t simple models although with higher gain. In essence, our innovative model is able to modify simple regression model, and hence, provide more promising results. In this regard, we apply big data analyses and techniques specifically in fashion field to analyze and make the salesprediction.
JournalArticle
  • Nari Arunraj
  • Diane Ahrens
A Hybrid Seasonal Autoregressive Integrated Moving Average and Quantile Regression for Daily Food Sales Forecasting , vol170
  • 2015

DOI: 10.1016/j.ijpe.2015.09.039

In the retail stage of a food supply chain, food waste and stock-outs occur mainly due to inaccurate forecasting of sales which leads to incorrect ordering of products. The time series sales in food retail industry are characterized by high volatility and skewness, which vary by time. So, the interval forecasts are required by the retail companies to set appropriate inventory policy (reorder point or safety stock level). This paper attempts to develop a seasonal autoregressive integrated moving average with external variables (SARIMAX) model to forecast daily sales of a perishable food. The process of fitting a SARIMAX model in this study involves: (i) the development of Seasonal Autoregressive Integrated Moving Average (SARIMA) model and (ii) combining the SARIMA model and the demand influencing factors using linear regression. As the SARIMAX using multiple linear regression (SARIMA-MLR) model produces only mean forecast, the possibility of underestimation and overestimation is very high due to high service level, peak, and sparse sales in food retail industry. Therefore, a hybrid SARIMA and Quantile Regression (SARIMA-QR) is developed to construct high and low quantile predictions. Instead of extrapolating the quantiles from the mean point forecasts of SARIMA-MLR model based on the assumption of normality, the SARIMA-QR model directly forecasts the quantiles. The developed SARIMA-MLR and SARIMA-QR models are applied in modeling and forecasting of sales data, i.e., the daily sales of banana from a discount retail store in Lower Bavaria, Germany. The results show that the SARIMA-MLR and -QR models yield better forecasts at out-sample data when compared to seasonal naïve forecasting, traditional SARIMA, and multi-layered perceptron neural network (MLPNN) models. Unlike the SARIMA-MLR model, the SARIMA-QR model provides better prediction intervals and a deep insight into the effects of demand influencing factors for different quantiles.
JournalArticle
  • Nari Arunraj
  • Diane Ahrens
A Hybrid Seasonal Autoregressive Integrated Moving Average and Quantile Regression for Daily Food Sales Forecasting , vol170
  • 2015

DOI: 10.1016/j.ijpe.2015.09.039

In the retail stage of a food supply chain, food waste and stock-outs occur mainly due to inaccurate forecasting of sales which leads to incorrect ordering of products. The time series sales in food retail industry are characterized by high volatility and skewness, which vary by time. So, the interval forecasts are required by the retail companies to set appropriate inventory policy (reorder point or safety stock level). This paper attempts to develop a seasonal autoregressive integrated moving average with external variables (SARIMAX) model to forecast daily sales of a perishable food. The process of fitting a SARIMAX model in this study involves: (i) the development of Seasonal Autoregressive Integrated Moving Average (SARIMA) model and (ii) combining the SARIMA model and the demand influencing factors using linear regression. As the SARIMAX using multiple linear regression (SARIMA-MLR) model produces only mean forecast, the possibility of underestimation and overestimation is very high due to high service level, peak, and sparse sales in food retail industry. Therefore, a hybrid SARIMA and Quantile Regression (SARIMA-QR) is developed to construct high and low quantile predictions. Instead of extrapolating the quantiles from the mean point forecasts of SARIMA-MLR model based on the assumption of normality, the SARIMA-QR model directly forecasts the quantiles. The developed SARIMA-MLR and SARIMA-QR models are applied in modeling and forecasting of sales data, i.e., the daily sales of banana from a discount retail store in Lower Bavaria, Germany. The results show that the SARIMA-MLR and -QR models yield better forecasts at out-sample data when compared to seasonal naïve forecasting, traditional SARIMA, and multi-layered perceptron neural network (MLPNN) models. Unlike the SARIMA-MLR model, the SARIMA-QR model provides better prediction intervals and a deep insight into the effects of demand influencing factors for different quantiles.
Lecture
  • Diane Ahrens
Dem Einkaufsverhalten auf der Spur - intelligente Prognosesysteme für den Lebensmitteleinzelhandel
  • 2014
Contribution
  • Nari Arunraj
  • Diane Ahrens
  • Michael Fernandes
  • M. Müller
Time series sales forecasting to reduce food waste in retail industry
  • 2014
Lecture
  • Nari Arunraj
  • Diane Ahrens
  • Michael Fernandes
  • Martin Müller
Time series sales forecasting to reduce food waste in retail industry
  • 2014
Lecture
  • Diane Ahrens
Dem Einkaufsverhalten auf der Spur - intelligente Prognosesysteme für den Lebensmitteleinzelhandel
  • 2014
Contribution
  • Nari Arunraj
  • Diane Ahrens
  • Michael Fernandes
  • M. Müller
Time series sales forecasting to reduce food waste in retail industry
  • 2014
Lecture
  • Nari Arunraj
  • Diane Ahrens
  • Michael Fernandes
  • Martin Müller
Time series sales forecasting to reduce food waste in retail industry
  • 2014
Contribution
  • R. Morvai
  • Z. Szegedi
  • Diane Ahrens
Present Day Problems of SME-Partnerships in Hungarian Food Supply Chains
  • 2013
Contribution
  • R. Morvai
  • Z. Szegedi
  • Diane Ahrens
Present Day Problems of SME-Partnerships in Hungarian Food Supply Chains
  • 2013
JournalArticle
  • Diane Ahrens
Internationaler Einkäufer. Immense Bedeutung für die Wettbewerbsfähigkeit von Unternehmen.
  • 2012
JournalArticle
  • Diane Ahrens
Internationaler Einkäufer. Immense Bedeutung für die Wettbewerbsfähigkeit von Unternehmen.
  • 2012
JournalArticle
  • Diane Ahrens
  • F. Schupp
  • B. Köppel
Nimm zwei
  • 2008
JournalArticle
  • Diane Ahrens
  • F. Schupp
  • B. Köppel
Nimm zwei
  • 2008
Book
  • Diane Ahrens
Terminplanung und -steuerung patientenbezogener Leistungen im Krankenhaus Dissertationsschrift (Universität Passau, 2000)
  • 2001
Book
  • Diane Ahrens
Terminplanung und -steuerung patientenbezogener Leistungen im Krankenhaus Dissertationsschrift (Universität Passau, 2000)
  • 2001
Lecture
  • Diane Ahrens
Zukunftsdörfer – Digitalisierung als Chance für den ländlichen Raum
Lecture
  • Diane Ahrens
Zukunftsdörfer – Digitalisierung als Chance für den ländlichen Raum

Labore

Technologie Campus Grafenau


Kernkompetenzen

Smart Region - Digitale Transformation im ländlichen Raum Supply Chain Planning & Design Einkauf und Beschaffungslogistik Produktions- und Distributionslogistik Prognose Internationales Management


Vita

Prof. Dr. Diane Ahrens studierte Betriebswirtschaftslehre an der Universität Passau, an der sie auch zum Dr. rer. pol. promovierte (2000). Industrieerfahrung im Bereich Einkauf und Logistik sammelte sie zunächst als Fachreferentin, später als Direktorin der Abteilung Policies and Programs der Zentralstelle Global Supply Chain and Procurement der Siemens AG in München. Sie kann auf Lehr- und Forschungserfahrung in China, Ungarn, Russland, Indien und Australien zurückblicken. 2003 wurde sie an die Hochschule Hof als Professorin für Internationale Unternehmensführung und Logistik sowie 2009 an die Technische Hochschule Deggendorf berufen. Neben ihrer Lehre leitet sie dort am Technologie Campus Grafenau ein 40-köpfiges Forschungsteam, spezialisiert auf Digitalisierung und Künstliche Intelligenz, das unter anderem drei der fünf digitalen Modelldörfer in Bayern betreut.