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Prof. Dr. Diane Ahrens

Applied Research in the following areas of expertise:

  • Smart Region: Digital transformation in rural areas
  • Business Data Analytics & Optimization
  • Applied Artificial Intelligence
  • AI for Context & Activity Recognition (AI for CARE)

Centre for Applied Research

Technology Campus Grafenau

Academic Director

Scientific Director

A 205

0991/3615-150

Sortierung:
Beitrag in Sammelwerk/Tagungsband

  • Sebastian Wilhelm
  • Dietmar Jakob
  • Jakob Kasbauer
  • Diane Ahrens

GeLaP: German Labeled Dataset for Power Consumption

In: Proceedings of Sixth International Congress on Information and Communication Technology. null (Lecture Notes in Networks and Systems) vol. 235 pg. 21-33.

Singapore

  • (2022)

DOI: 10.1007/978-981-16-2377-6_5

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.
  • TC Grafenau
  • DIGITAL
Vortrag

  • Diane Ahrens

Impulsvortrag: „Digitalisierung im ländlichen Raum“

In: Online-Reihe „Demokratie im Gespräch“ : „Digitalisierung im ländlichen Raum“

Akademie für politische Bildung Tutzing in Kooperation mit der Bayerischen Landeszentrale für politische Bildungsarbeit und dem Bayerischen Volkshochschulverband e.V. Online

  • 09.03.2022 (2022)
  • TC Grafenau
  • DIGITAL
Vortrag

  • Diane Ahrens

Digital Health als Chance für den ländlichen Raum: Erfahrung aus dem Projekt „MeDiLand“. Keynote

In: 1. Symposium des Gesundheitscampus Bad Kötzting

Technische Hochschule Deggendorf Bad Kötzting

  • 15.02.2022 (2022)
  • TC Grafenau
  • GESUND
  • DIGITAL
Vortrag

  • Diane Ahrens

Keynote „Digitalisierung in Bayerischen Kommunen“

In: Netzwerkkonferenz „Digitalisierung in Bayerischen Kommunen“

Bayerisches Staatsministerium für Digitales München

  • 24.11.2022 (2022)
  • TC Grafenau
  • DIGITAL
Vortrag

  • Diane Ahrens

Smarte Kommunen für eine lebenswerte Zukunft. Ländliche Entwicklung unterstützt digitale Transformation von Kommunen

In: Wintervortragsreihe der DVW Bayern e.V. Gesellschaft für Geodäsie, Geoinformation und Landmanagement

Online

  • 18.02.2022 (2022)
  • TC Grafenau
  • DIGITAL
Zeitschriftenartikel

  • M. Alnahhal
  • Diane Ahrens
  • B. Salah

Dynamic Lead-Time Forecasting Using Machine Learning in a Make-to-Order Supply Chain

In: Applied Sciences vol. 11 pg. 10105.

  • (2021)

DOI: 10.3390/app112110105

This paper investigates the dynamic forecasting of lead-time, which can be performed by a logistics company for optimizing temporal shipment consolidation. Shipment consolidation is usually utilized to reduce outbound shipments costs, but it can increase the lead time. Forecasting in this paper is performed in a make-to-order supply chain using real data, where the logistics company does not know the internal production data of manufacturers. Forecasting was performed in several steps using machine-learning methods such as linear regression and logistic regression. The last step checks if the order will come in the next delivery week or not. Forecasting is evaluated after each shipment delivery to check the possibility of delaying the current arriving orders for a certain customer until the next week or making the delivery to the customer immediately. The results showed reasonable accuracy expressed in different ways, and one of them depends on a type I error with an average value of 0.07. This is the first paper that performs dynamic forecasting for the purpose of shipment temporal consolidation optimization in the consolidation center.
  • TC Grafenau
  • DIGITAL
Zeitschriftenartikel

  • M. 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

In: Annals of Operations Research

  • (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.
  • Angewandte Wirtschaftswissenschaften
  • TC Grafenau
  • MOBIL
  • DIGITAL
Zeitschriftenartikel

  • Lisa-Marie Hanninger
  • Jessica Laxa
  • Diane Ahrens

A roadmap to becoming a smart village: Experiences from living labs in rural Bavaria

In: eJournal of eDemocracy & Open Government (JeDEM) vol. 13 pg. 89-109.

  • (2021)

DOI: 10.29379/jedem.v13i2.635

This paper illustrates the measures and digital integrations being made in the course of digitalization, using the example of existing rural pilot communities in Bavaria, Germany. The participating communities were selected as part of the government-funded project "Digitales Dorf" (Engl. digital village). Since 2016, digital solutions as well as complementary actions have been identified and implemented to make everyday life in the community equal to that in the city: the main intention is to push digitalization to create equivalent living conditions to urban areas. This paper is intended to provide an overview of the requirements and steps that need to be taken in digital transformation, in order to develop a generalized blueprint for other communities. Furthermore, it introduces the pilot projects, provides an insight into best practices to promote digitalization in traditional rural areas, and focuses on the transformation process rather than on digital solutions.
  • TC Grafenau
  • DIGITAL
Vortrag

  • Diane Ahrens

Gibt es schon viele Digitale Dörfer?

In: Smart Villages – War das nicht die Verheißung des Internet?

Deutsche ict + Medienakademie Köln

  • 10.11.2021 (2021)
  • Angewandte Informatik
  • TC Grafenau
  • DIGITAL
Beitrag in Sammelwerk/Tagungsband

  • Dietmar Jakob
  • Sebastian Wilhelm
  • A. Gerl
  • Diane Ahrens

A Quantitative Study on Awareness, Usage and Reservations of Voice Control Interfaces by Elderly People

In: Proceedings of the 23rd International Conference on Human-Computer Interaction (HCII 2021) [July 24–29, 2021; online]. null (Lecture Notes in Computer Science (LNCS))

  • (2021)
One third of Germans talk to ’Alexa’, ’Siri’ and other voice-controlled devices. These devices become omnipresent and change the way how humans interact with digital technologies. We hypothesize, this Human-Computer-Interface can minimize barriers for elderly people in their usage of digital services. But, do elderly people even know about voice-controlled technologies? Are the systems used by elderly and what reservations do they have? Based on a quantitative study in three municipalities in a rural area (n = 747), we found that 59% of people aged 55+ years know voice-controlled devices, 37% used them at least once and even 26% use them regularly. But, more than two thirds of respondents (69%) are concerned that their data are not safe. In contrast, only 35% express concerns to be unable to use the devices. The study concludes that there is a gap between the perceived usefulness and trust in the devices for the surveyed demographic.
  • TC Grafenau
  • DIGITAL
Zeitschriftenartikel

  • M. Alnahhal
  • Diane Ahrens
  • B. Salah

Modeling Freight Consolidation in a Make-to-Order Supply Chain: A Simulation Approach

In: Processes vol. 9 pg. 1554.

  • (2021)

DOI: 10.3390/pr9091554

Shipment consolidation is one of main initiatives to reduce CO2 emissions and transportation cost. It reduces the number of shipments per customer and reduces transportation costs by using larger shipments. This paper investigates the temporal consolidation process in a central consolidation center in a make-to-order supply chain. This research was motivated by a case study of a design furniture company that has many suppliers and customers in large parts of Europe. Simulation was used to check the effect of a new and a special time-based temporal consolidation on the response time in outbound logistics. A soft delivery deadline that is less than the average lead time was used because of the long lead time. Arena Software was used to model the supply chain in order to find the best circumstances to use consolidation. Results showed that temporal consolidation could be more effective when order preparation time is with larger variability. The useful waiting is more when there is at least one order every four days. A formula that approximates the percent of reduced shipments was found. Furthermore, many shipments can be reduced without severely affecting the average response time. The value of the study is that it investigates consolidation problems in a high-mix low-volume environment that was overlooked by previous research.
  • Angewandte Wirtschaftswissenschaften
  • TC Grafenau
  • DIGITAL
Beitrag in Sammelwerk/Tagungsband

  • Diane Ahrens

Digitalisierung als Thema der Integrierten Ländlichen Entwicklung

In: Neue Wege digital und analog! Den Wandel im ländlichen Raum aktiv gestalten. null (Schriftenreihe der Deutschen Landeskulturgesellschaft) pg. 15-36.

  • (2021)
  • TC Grafenau
  • DIGITAL
Vortrag

  • Diane Ahrens

Digitale Dörfer - Digitalisierung als Chance für den Ländlichen Raum. Keynote

In: Smart Region - Erste Schritte in die Smart Region Herzogtum Lauenburg

Smart City Operations GmbH, Digitale Wirtschaft in Schleswig-Holstein e.V, Wirtschaftsförderung Kreis Herzogtum-Lauenburg

  • 25.11.2021 (2021)
  • TC Grafenau
  • Angewandte Wirtschaftswissenschaften
  • DIGITAL
Vortrag

  • Diane Ahrens

Digitale Dörfer: ein Erfahrungsbericht aus Bayern

In: Veranstaltung des Innenministeriums Schleswig-Holstein/Digitale Kommune: „Ihr Flugtaxi ist da – Auswirkungen der Digitalisierung auf unser Stadt- und Dorfleben“

Online

  • 19.08.2021 (2021)
  • TC Grafenau
  • Angewandte Wirtschaftswissenschaften
  • DIGITAL
Beitrag in Sammelwerk/Tagungsband

  • Bernhard Daffner
  • Michael Scholz
  • Jörg Bauer
  • Diane Ahrens

Kapazitäts- und zeitrestringierte Tourenplanung am Beispiel eines mittelständischen Großhändlers

In: Jahrbuch der Logistikforschung. null (Schriftenreihe Logistikforschung) pg. 207-218.

Linz, Österreich

  • (2021)
  • TC Grafenau
  • DIGITAL
  • MOBIL
Vortrag

  • Diane Ahrens

Digitalisierung als Thema der Integrierten Ländlichen Entwicklung

In: Internationale Infrastrukturtagung, 41. Bundestagung der Deutschen Landeskulturgesellschaft

Wiesbaden

  • 13.10.2021 (2021)
  • TC Grafenau
  • DIGITAL
Vortrag

  • Domenic Sommer
  • Rainer Bomeisl
  • Diane Ahrens

Projekt MeDiLand: Medizin Digital zur Verbesserung der Versorgung auf dem Land . Abstract und Poster

In: DIGITAL MEDIZIN - ERKENNEN, VERSTEHEN, HEILEN: 66. GMDS-Jahrestagung/12. TMF-Jahreskongress 2021

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. digital

  • 26.-30.09.2021 (2021)
  • TC Grafenau
  • DIGITAL
  • GESUND
Zeitschriftenartikel

  • Domenic Sommer
  • Rainer Bomeisl
  • Diane Ahrens

Projekt: Medizin Digital zur Verbesserung der stationären Pflege auf dem Land. Technik überbrückt Distanzen

In: CAREkonkret pg. 8.

  • (2021)
Informations- und Kommunikationstechnik überbrückt Distanzen zwischen Patienten, Ärzten und Pflegekräften. Während in anderen Studien häufig Einzelanwendungen wie Telediagnostik, Telemonitoring oder Tele-Notärzte untersucht wurden, verfolgte das Projekt MeDiLand einen ganzheitlichen Ansatz. In dem vom Bayerischen Gesundheitsministerium geförderten Projekt (Juli 2018 bis Oktober 2020) wurden neue digitale Wege zur Verbesserung der Gesundheitsversorgung auf dem Land erprobt sowieu.a. Use Cases für den Telemedizineinsatz in der stationären Pflege erfasst. So wurden in einem intersektoralen, patientenzentrierten Netzwerk aus Arztpraxen, Pflegeheimen, Pflegediensten, Kliniken und einer Bergschutzhütte die audiovisuelle Kommunikation und die Übertragung von Vitaldaten erprobt. Neben der Vernetzung heterogener Leistungserbringer sind diverse Use Cases in der Pflege hervorzuheben.
  • TC Grafenau
  • GESUND
  • DIGITAL
Vortrag

  • Diane Ahrens

Zukunftsdörfer – Digitalisierung als Chance für den ländlichen Raum

In: 14. Zukunftsforum Ländliche Entwicklung „Alles digital oder doch wieder „normal“? Neue Formen von Arbeit und Teilhabe als Chance für die Ländlichen Räume“

Gemeinsames Fachforum der Bund-Länder-Arbeitsgemeinschaft Nachhaltige Landentwicklung (ArgeLandentwicklung) und der Deutschen Landeskulturgesellschaft

  • 20.01.2021 (2021)
  • TC Grafenau
  • Angewandte Wirtschaftswissenschaften
  • DIGITAL
Vortrag

  • Diane Ahrens

Teilnahme Diskussionspanel „Digitale Kompetenzen als Schlüssel für den Erfolg“

In: Netzwerk-Jahreskonferenz des Netzwerk Zukunftsraum Land LE 14-20

Online

  • 26.11.2020 (2020)
  • TC Grafenau
  • Angewandte Wirtschaftswissenschaften
  • DIGITAL
Monographie

  • 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.
  • TC Grafenau
  • DIGITAL
Beitrag in Sammelwerk/Tagungsband

  • Sebastian Wilhelm
  • Dietmar Jakob
  • Jakob Kasbauer
  • Melanie Dietmeier
  • A. Gerl
  • Benedikt Elser
  • Diane Ahrens

Organizational, Technical, Ethical and Legal Requirements of Capturing Household Electricity Data for Use as an AAL System

In: Proceedings of the Fifth International Congress on Information and Communication Technology (ICPCCI 2019) [20-21 February 2020; London, UK]. null (Advances in Intelligent Systems and Computing)

Singapore

  • (2020)

DOI: 10.1007/978-981-15-5856-6_38

Due to demographic change elderly care is one of the major challenges for society in near future fostering new services to support and enhance the life quality of the elderly generation. A particular aspect is the desire to live in one’s homes instead of hospitals and retirement homes as long as possible. Therefore it is essential to monitor the health status i.e. the activity of the individual. In our data-driven society data is collected at an increasing rate enabling personalized services for our daily life using machine-learning and data mining technologies. However the lack of labeled datasets from a realistic environment hampers research for training and evaluating algorithms. In the project BLADL we use data mining technologies to gauge the health status of elderly people. Within this work we discuss the challenges and caveats both from a technical and ethical perspectives to create such a dataset.
  • TC Grafenau
  • Angewandte Wirtschaftswissenschaften
  • DIGITAL
Beitrag in Sammelwerk/Tagungsband

  • Lisa-Marie Hanninger
  • Jessica Laxa
  • Diane Ahrens

Rural Areas on Their Way to a Smart Village - Experiences from Living Labs in Bavaria

pg. 107-119.

Maribor

  • (2020)
  • TC Grafenau
  • DIGITAL
Vortrag

  • Diane Ahrens

Ride hailing, car sharing and the car ownership of tomorrow: operating in the shifting global shared mobility market

In: Middle East Rail & Smart Mobility Conference 2020

Dubai, VAE

  • 25.02.2020 (2020)
  • Angewandte Wirtschaftswissenschaften
  • TC Grafenau
  • MOBIL
Beitrag in Sammelwerk/Tagungsband

  • Sebastian Wilhelm
  • Dietmar Jakob
  • Diane Ahrens

Human Presence Detection by monitoring the indoor CO2 concentration

pg. 199-203.

  • (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.
  • Angewandte Wirtschaftswissenschaften
  • TC Grafenau
  • DIGITAL
Vortrag

  • Diane Ahrens

Vortrag zum laufenden Projekt „MeDiLand – Medizin Digital zur Verbesserung der Versorgung auf dem Land“

In: 3. Niederbayerischer Fachkongress zum Thema "Energiewende in Niederbayern"

München

  • 23.01.2020 (2020)
  • Angewandte Wirtschaftswissenschaften
  • TC Grafenau
  • DIGITAL
  • GESUND
Vortrag

  • Diane Ahrens

Räumliche Unabhängigkeit Dank Digitalisierung

In: Sommerkolloquium 2020 der Bayerischen Akademie Ländlicher Raum und Akademie für Politik und Zeitgeschehen der Hanns-Seidel-Stiftung „Corona und die große Transformation: Perspektiven für die ländlichen Räume?“

München

  • 13.07.2020 (2020)
  • TC Grafenau
  • Angewandte Wirtschaftswissenschaften
  • DIGITAL
Beitrag in Sammelwerk/Tagungsband

  • Diane Ahrens

Digitale Dörfer. Gleichwertige Lebensverhältnisse durch Digitalisierung im ländlichen Raum?

pg. 65-78.

München

  • (2020)
  • TC Grafenau
  • Angewandte Wirtschaftswissenschaften
  • DIGITAL
Zeitschriftenartikel

  • Ali Fallah-Tehrani
  • M. Strickert
  • Diane Ahrens

Class of Monotone Kernelized Classifiers on the basis of the Choquet Integral

In: Expert Systems vol. 37 pg. 1-15.

  • (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.
  • Angewandte Informatik
  • TC Grafenau
  • DIGITAL
Vortrag

  • Diane Ahrens

Digitales Dorf - "Alles Smart!?"

In: Rotunde der Salzburg AG

Salzburg, Österreich

  • 25.02.2019 (2019)
  • Angewandte Informatik
  • TC Grafenau
  • DIGITAL
Vortrag

  • Diane Ahrens
  • Bernhard Bauer

Prognose und Monitoring in der Gemeinschaftsverpflegung - Optimierter Wareneinsatz durch Big Data

In: 12. Wissenschaftliches Symposium "Supply Management"

Bundesverband Materialwirtschaft, Einkauf und Logistik Mannheim

  • 26.03.2019 (2019)
  • TC Grafenau
  • Angewandte Informatik
  • NACHHALTIG
  • DIGITAL
Zeitschriftenartikel

  • Diane Ahrens
  • Dietmar Jakob

Digitale Wege erkunden mit BLADL

In: Der Bayerische Bürgermeister vol. 102 pg. 160-161.

  • (2019)
  • TC Grafenau
  • Angewandte Informatik
  • DIGITAL
Zeitschriftenartikel

  • Diane Ahrens
  • Stefanie Seidenhofer
  • Gudrun Fischer

Die Technik macht es möglich

In: Altenheim - Lösungen fürs Management pg. 42-44.

  • (2019)
  • Angewandte Informatik
  • TC Grafenau
  • DIGITAL
Monographie

  • 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)
  • Angewandte Informatik
  • TC Grafenau
  • DIGITAL
Vortrag

  • Diane Ahrens

Zukunftsdörfer - Digitalisierung als Chance für den ländlichen Raum . Keynote

In: Akademiegespräch

Bayerischer Landtag München

  • 09.04.2019 (2019)
  • TC Grafenau
  • Angewandte Informatik
  • DIGITAL
Vortrag

  • Diane Ahrens

Digitalisierung des ländlichen Raums

In: Podiumsdiskussion der Friedrich-Naumann-Stiftung für die Freiheit

Landshut

  • 18.09.2019 (2019)
  • Angewandte Informatik
  • TC Grafenau
  • DIGITAL
Vortrag

  • Diane Ahrens

Digitale Daseinsfürsorge in Stadt und Land

Akademie für Politische Bildung Tutzing Tutzing

  • 01.02.2019 (2019)
  • Angewandte Informatik
  • TC Grafenau
  • DIGITAL
Zeitschriftenartikel

  • Diane Ahrens
  • Sandra Gabert

Ländliche Wege in die digitale Zukunft . Was ist bislang in den digitalen Dörfern passiert?

In: Der Bayerische Bürgermeister vol. 102 pg. 155-159.

  • (2019)
  • TC Grafenau
  • Angewandte Informatik
  • DIGITAL
Vortrag

  • Diane Ahrens

Zukunftsdörfer: Digitalisierung im ländlichen Raum

In: IX. TOP Management Programm der Bayerischen Staatskanzlei

St. Quirin am Tegernsee

  • 21.11.2019 (2019)
  • TC Grafenau
  • Angewandte Informatik
  • DIGITAL
Vortrag

  • Diane Ahrens

Smart Villages in Bavaria: A Living Lab Approach to Prevent Urbanization

In: International Workshop of the Arab-German Young Academy of Sciences and Humanities

Ras Al Khaimah, Vereinigte Arabische Emirate

  • 06.11.2019 (2019)
  • TC Grafenau
  • Angewandte Informatik
  • DIGITAL
Beitrag in Sammelwerk/Tagungsband

  • Bernhard Bauer
  • Diane Ahrens

Datenbasierte Gäste- und Speiseprognosen in der Gemeinschaftsverpflegung

In: Supply Management Research: Aktuelle Forschungsergebnisse 2019. Gleichzeitig Tagungsband zum 12. Wissenschaftlichen Symposium 'Supply Management' an der Universität Mannheim (25.-26.03.2019) (Advanced Studies in Supply Management) pg. 225-245.

  • (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.
  • Angewandte Informatik
  • TC Grafenau
  • DIGITAL
Vortrag

  • Diane Ahrens

Digitalisierung als Chance für ländliche Gemeinden

In: Kommunalforum Alpenraum

Kundl in Tirol, Österreich

  • 15.03.2018 (2018)
  • TC Grafenau
  • Angewandte Informatik
  • DIGITAL
Zeitschriftenartikel

  • Diane Ahrens

Frauenau und Spiegelau werden digital

In: Der Bayerische Bürgermeister vol. 71 pg. 290-293.

  • (2018)
  • TC Grafenau
  • Angewandte Wirtschaftswissenschaften
  • DIGITAL
Beitrag in Sammelwerk/Tagungsband

  • Ali Fallah-Tehrani
  • Diane Ahrens

Enhanced Predictive Models for Purchasing in the Fashion Field by Applying Regression Trees Equipped with Ordinal Logistic Regression

In: Artificial Intelligence for Fashion Industry in the Big Data Era. null (Springer Series in Fashion Business) pg. 27-45.

Singapore

  • (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.
  • TC Grafenau
  • Angewandte Wirtschaftswissenschaften
  • DIGITAL
Vortrag

  • Diane Ahrens

Anhörung als Sachverständige im Bayerischen Landtag zum Thema "Sicherung der wohnortnahen Versorgung in der Kommune"

Ausschuss für Kommunale Fragen, Innere Sicherheit und Sport München

  • 25.04.2018 (2018)
  • Angewandte Informatik
  • TC Grafenau
  • DIGITAL
Vortrag

  • Diane Ahrens

Digitales Dorf

In: Symposium des Bayerischen Staatsministeriums für Familie, Arbeit und Soziales

Nürnberg

  • 17.09.2018 (2018)
  • TC Grafenau
  • Angewandte Informatik
  • DIGITAL
Vortrag

  • Diane Ahrens

Digitalisierung als Chance für ländliche Gemeinden

In: Netzwerktreffen Digitaler Lebensraum - Agenda 21

Netzwerk Oberösterreich Vorchdorf, Oberösterreich

  • 19.03.2018 (2018)
  • Angewandte Informatik
  • TC Grafenau
  • DIGITAL
Vortrag

  • Diane Ahrens

Mega-Trend Digitalisierung

In: Sparkassenkommunalforum

Grafenau

  • 16.11.2018 (2018)
  • Angewandte Informatik
  • TC Grafenau
  • DIGITAL
Vortrag

  • Diane Ahrens

Digitales Dorf - Gemeinsam digitale Zukunft schaffen

In: Referentenklausur

Bayerischer Städtetag Bad Aibling

  • 19.04.2018 (2018)
  • TC Grafenau
  • Angewandte Informatik
  • DIGITAL
Vortrag

  • Diane Ahrens

Digitales Dorf

In: Kommunale Zukunftsgespräche "Digitalisierung als Chance für den ländlichen Raum"

Linz, Österreich

  • 22.10.2018 (2018)
  • TC Grafenau
  • Angewandte Informatik
  • DIGITAL
Vortrag

  • Diane Ahrens

Digitale Hörnerdörfer

In: Infoveranstaltung zum Wettbewerb "Digitale Hörnerdörfer"

  • 06.03.2018 (2018)
  • TC Grafenau
  • Angewandte Informatik
  • DIGITAL
Zeitschriftenartikel

  • Mohammed Alnahhal
  • Diane Ahrens

A Simulation-Based System for Calculating Optimal Numbers of Forklift Drivers in Industrial Plants

In: Bavarian Journal of Applied Sciences vol. 4 pg. 354-369.

  • (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.
  • Angewandte Wirtschaftswissenschaften
  • TC Grafenau
  • MOBIL
Vortrag

  • Diane Ahrens

Digitale Hörnerdörfer

In: Auftaktveranstaltung "Digitale Hörnerdörfer"

Balderschwang/Obermaiselstein

  • 10.09.2018 (2018)
  • Angewandte Informatik
  • TC Grafenau
  • DIGITAL
Vortrag

  • Diane Ahrens

Prognosen im Lebensmittelkonsum: Weniger Lebensmittelverluste durch Optimierung von Prognosen und Disposition

In: Prognosekonferenz

Grafenau

  • 06.11.2017 (2017)
  • TC Grafenau
  • Angewandte Informatik
  • DIGITAL
Vortrag

  • Diane Ahrens

Digitales Dorf - Von der Vision zur Modellregion

Wirtschafts- und Verkehrsausschuss des Bayerischen Landkreistages Neuruppin

  • 25.04.2017 (2017)
  • TC Grafenau
  • Angewandte Informatik
  • DIGITAL
Vortrag

  • Diane Ahrens

Digitalisierung als Chance für ländliche Gemeinden

In: Netzwerktreffen Digitaler Lebensraum - Agenda 21

Netzwerk Oberösterreich Stift Reichersberg, Hausruck/Innviertel, Österreich

  • 02.10.2017 (2017)
  • TC Grafenau
  • Angewandte Informatik
  • DIGITAL
Vortrag

  • Diane Ahrens

Digitalisierung als Chance für ländliche Gemeinden

In: Netzwerktreffen Digitaler Lebensraum - Agenda 21

Netzwerk Oberösterreich Kirchschlag bei Linz, Oberösterreich

  • 23.10.2017 (2017)
  • Angewandte Informatik
  • TC Grafenau
  • DIGITAL
Beitrag in Sammelwerk/Tagungsband

  • Nari Arunraj
  • Diane Ahrens

Improving food supply chain using hybrid semiparametric regression model

In: Supply Management Research. Aktuelle Forschungsergebnisse 2016 (Advanced Studies in Supply Management) pg. 213-238.

Wiesbaden

  • (2017)
  • TC Grafenau
  • NACHHALTIG
Vortrag

  • Diane Ahrens

Digitales Dorf - Von der Vision zur Modellregion

Wirtschafts- und Verkehrsausschuss des Bayerischen Landkreistages Weißenburg i. Bay.

  • 20.06.2017 (2017)
  • Angewandte Informatik
  • TC Grafenau
  • DIGITAL
Zeitschriftenartikel

  • Ali Fallah-Tehrani
  • Diane Ahrens

Modified Sequential k‐means Clustering by Utilizing Response: A Case Study for Fashion Products

In: Expert Systems vol. 34 pg. e12226.

  • http://onlinelibrary.wiley.com/doi/10.1111/exsy.12226/abstract (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.
  • TC Grafenau
  • NACHHALTIG
  • DIGITAL
Vortrag

  • Diane Ahrens

Märkte im Wandel - Anforderungen an die Logistik

In: Regensburger Verkehrskongress

Regensburg

  • 09.10.2017 (2017)
  • TC Grafenau
  • Angewandte Informatik
  • DIGITAL
Zeitschriftenartikel

  • Ali Fallah-Tehrani
  • Diane Ahrens

Modeling Label Dependence for Multi-Label Classification Using the Choquistic Regression

In: Pattern Recognition Letters vol. 92 pg. 75-80.

  • (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.
  • TC Grafenau
  • DIGITAL
Vortrag

  • Diane Ahrens

Digital Village - A Bavarian Initiative

In: Rural Summit 2017

Eindhoven, Niederlande

  • 23.03.2017 (2017)
  • Angewandte Informatik
  • TC Grafenau
  • DIGITAL
Zeitschriftenartikel

  • Nari Arunraj
  • Diane Ahrens

Estimation of Non-Catastrophic Weather Impacts for Retail Industry

In: International Journal of Retail & Distribution Management vol. 44 pg. 731-753.

  • (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.
  • Angewandte Wirtschaftswissenschaften
  • DIGITAL
Vortrag

  • Diane Ahrens

Intelligente Warenwirtschaftssysteme mit praktischen Umsetzungsbeispielen im Handel

In: KErn Tagung "Energie sparen - Ressourcen schonen, Lebensmittel als Energieressource

Technische Hochschule Deggendorf Bayerisches Staatsministerium für Ernährung, Landwirtschaft und Forsten

  • 17.02.2016 (2016)
  • Angewandte Wirtschaftswissenschaften
Zeitschriftenartikel

  • Diane Ahrens
  • I. Häberle
  • P. Muranyi

Zu große Energiemengen landen im Müll. Energieverluste durch Lebensmittelverschwendung - Experten zeigen Einsparpotentiale auf

In: Fleischwirtschaft vol. 96 pg. 70-75.

  • (2016)
  • TC Grafenau
  • Angewandte Informatik
  • NACHHALTIG
Vortrag

  • Diane Ahrens

Impulses for Economics and Region by Means of Decentralization of Research

In: VSERS International Scholarly Conference

České Budějovice, Tschechien

  • 31.03.-01.04.2016 (2016)
  • Angewandte Wirtschaftswissenschaften
Beitrag in Sammelwerk/Tagungsband

  • Ali Fallah-Tehrani
  • Diane Ahrens

Improved Forecasting and Purchasing of Fashion Products based on the Use of Big Data Techniques

In: Supply Management Research. Aktuelle Forschungsergebnisse 2015 (Advanced Studies in Supply Management) pg. 293-310.

Wiesbaden

  • (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.
  • TC Grafenau
  • NACHHALTIG
  • DIGITAL
Zeitschriftenartikel

  • Ali Fallah-Tehrani
  • Diane Ahrens

Supervised Regression Clustering: A Case Study for Fashion Products

In: International Journal of Business Analytics (IJBAN) vol. 3 pg. 21-40.

  • (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.
  • TC Grafenau
  • DIGITAL
Zeitschriftenartikel

  • Ali Fallah-Tehrani
  • Diane Ahrens

Enhanced predictive models for purchasing in the fashion field by using kernel machine regression equipped with ordinal logistic regression

In: Journal of Retailing and Consumer Services vol. 32 pg. 131-138.

  • (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.
  • TC Grafenau
  • DIGITAL
Zeitschriftenartikel

  • Nari Arunraj
  • Diane Ahrens
  • Michael Fernandes

Application of SARIMAX model to forecast daily sales in retail industry

In: International Journal of Operations Research and Information Systems (IJORIS) vol. 7 pg. 1-20.

  • (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.
  • TC Grafenau
  • DIGITAL
Zeitschriftenartikel

  • Nari Arunraj
  • Diane Ahrens

A Hybrid Seasonal Autoregressive Integrated Moving Average and Quantile Regression for Daily Food Sales Forecasting

In: International Journal of Production Economics vol. 170 pg. 321-335.

  • (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.
  • TC Grafenau
  • NACHHALTIG
Beitrag in Sammelwerk/Tagungsband

  • Nari Arunraj
  • Diane Ahrens
  • Michael Fernandes
  • M. Müller

Time series sales forecasting to reduce food waste in retail industry

  • (2014)
  • TC Grafenau
Vortrag

  • Nari Arunraj
  • Diane Ahrens
  • Michael Fernandes
  • Martin Müller

Time series sales forecasting to reduce food waste in retail industry

In: 34th International Symposium on Forecasting

Rotterdam

  • 29.06.-02.07.2014 (2014)
  • Angewandte Informatik
  • TC Grafenau
Vortrag

  • Diane Ahrens

Dem Einkaufsverhalten auf der Spur - intelligente Prognosesysteme für den Lebensmitteleinzelhandel

In: KErn Fachsymposium: Restlos Gut Essen - Nachhaltige Ernährung im 21. Jahrhundert

München/Kulmbach

  • 24.07.2014 (2014)
  • Angewandte Wirtschaftswissenschaften
Beitrag in Sammelwerk/Tagungsband

  • R. Morvai
  • Z. Szegedi
  • Diane Ahrens

Present Day Problems of SME-Partnerships in Hungarian Food Supply Chains

pg. 95-104.

  • (2013)
  • TC Grafenau
Zeitschriftenartikel

  • Diane Ahrens

Internationaler Einkäufer. Immense Bedeutung für die Wettbewerbsfähigkeit von Unternehmen.

In: All About Sourcing pg. 21.

  • (2012)
  • Angewandte Wirtschaftswissenschaften
Zeitschriftenartikel

  • Diane Ahrens
  • F. Schupp
  • B. Köppel

Nimm zwei

In: LOG.Punkt pg. 44-45.

  • (2008)
  • Angewandte Wirtschaftswissenschaften
Monographie

  • Diane Ahrens

Terminplanung und -steuerung patientenbezogener Leistungen im Krankenhaus . Dissertationsschrift (Universität Passau, 2000)

In: Berichte aus der Betriebswirtschaft

Shaker Aachen

  • (2001)
  • Angewandte Wirtschaftswissenschaften

labs

Technology Campus Grafenau - Applied Research Institution for Digitalization and AI

core competencies

Smart Region - Digital Transformation in Rural Areas Supply Chain Planning & Design Global and Strategic Sourcing, Procurement Logistics Productions- and Distribution Logistics Business Forecasting International Management

Vita

Prof. Dr. Diane Ahrens is professor for international management at Deggendorf Institute of Technology, Germany since 2009. In 2012 she founded and actually leads a 40-person research team specialized on supply chain management, data analytics and artificial intelligence applications, as well as smart region projects. In close collaboration with several Bavarian ministries she initiated and develops three “smart villages”, living labs. She teaches courses in procurement, supply chain, and process management as well as international management, can look back on teaching and research stays in China, India, Russia, Hungary, and Australia, and is a frequently invited key note speaker in Germany and abroad. She studied Business Administration and Economics at Passau University, Germany and in China. After having received her PhD about sequencing and scheduling at University of Passau in 1999, she started her professional career with Siemens AG, Corporate Department Global Supply Chain and Procurement with worldwide responsibility for logistics projects and standards. In 2003, she was appointed professor of international and logistics Management at Hof University of Applied Sciences.