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Computers
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IT in Production and Logistics
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IT in Production and Logistics

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "Internet of Things (IoT) and Industrial IoT".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 4783

Special Issue Editors

Prof. Dr. Markus Rabe

E-Mail Website
Guest Editor
Department of IT in Production and Logistics, TU Dortmund University, 44227 Dortmund, Germany
Interests: computer simulation; logistics assistance systems; industrial IT systems; business processes; intralogistics; supply chains; urban last-mile delivery; data farming and data analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Anne Antonia Scheidler

E-Mail Website
Guest Editor
Department of IT in Production and Logistics, TU Dortmund University, 44227 Dortmund, Germany
Interests: logistics; supply chains; knowledge discovery in databases; data mining; data preprocessing; discrete event simulation; big data; information representation
Prof. Dr. Marc Stautner

E-Mail Website
Guest Editor
Institute of Mechanical Engineering, Hochschule Ruhr West, University of Applied Sciences, 45479 Mülheim an der Ruhr, Germany
Interests: digitalization; process simulation; robotics; additive and subtractive manufacturing; CAD; CAM; algorithmic optimization
Prof. Dr. Simon J. E. Taylor

E-Mail Website
Guest Editor
Department of Computer Science, Brunel University London, Uxbridge UB8 3PN, UK
Interests: new technologies and simulation including high-performance computing; digital twins and AI; simulation in international development; open science in simulation; production & logistics applications of simulation

Special Issue Information

Dear Colleagues,

The importance of IT in the design and implementation of industrial systems is continuously increasing, and some systems for innovative products or processes already have a higher value content in IT than in hardware like machines and other equipment. Software development tasks are ubiquitous in enterprises, but still most IT projects do not achieve the positive aspects that have been promised when deciding their budgets, and much worse, a significant part of these projects actually fail and are cancelled or result in software systems that are never productively used. Therefore, it is mandatory for engineers to understand the chances but also the challenges and risks of the many IT technologies that are available today. This Special Issue is calling for contributions on applications of up-to-date technologies, discussing their preconditions, their pros and cons, procedure models for the application of these new technologies and any further aspects that can support readers in successfully applying these technologies in their enterprises as well as furthering cutting-edge research.

We invite papers covering the following aspects of Information Technology applied to production and logistics, among other relevant topics:

  • IT for production logistics;
  • IT for intralogistics systems;
  • Supply chain management;
  • IT for freight management, roads/ ports/ air cargo;
  • IT for mobility and urban logistics;
  • Modeling of business processes and process mining;
  • Material flow simulation and data farming;
  • Digital factory and digital twin;
  • Auto-ID technology and applications, Internet of Things;
  • Design of industrial IT systems, industrial software engineering;
  • Innovative databases for monitoring and control;
  • Data mining and data modeling;
  • Input data management and data quality for industrial applications;
  • Advanced industrial AI applications;
  • Industrial web applications.

Prof. Dr. Markus Rabe
Dr. Anne Antonia Scheidler
Prof. Dr. Marc Stautner
Prof. Dr. Simon J. E. Taylor
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • production
  • logistics
  • business processes
  • industrial IT systems
  • material flow simulation
  • industrial web applications
  • data modeling and data quality
  • innovative databases
  • process mining
  • industrial software development
  • Internet of Things
  • data mining
  • auto ID applications
  • digital twin

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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23 pages, 1535 KiB  
Article
Review of Maturity Models for Data Mining and Proposal of a Data Preparation Maturity Model Prototype for Data Mining
by Florian Hochkamp, Anne Antonia Scheidler and Markus Rabe
Computers 2025, 14(4), 146; https://doi.org/10.3390/computers14040146 - 11 Apr 2025
Viewed by 336
Abstract
Companies face uncertainties evaluating their own capabilities when implementing data analytics or data mining. Data mining is a valuable process used to analyze data and support decisions based on the knowledge generated, creating a pipeline from the phases of data collection to data [...] Read more.
Companies face uncertainties evaluating their own capabilities when implementing data analytics or data mining. Data mining is a valuable process used to analyze data and support decisions based on the knowledge generated, creating a pipeline from the phases of data collection to data preparation and data mining. In order to assess the target state of a company in terms of its data mining capabilities, maturity models are viable tools. However, no maturity models exist that focus on the data mining process and its particular phases. This article discusses existing maturity models in the broader field of data mining, such as data management, machine learning, or artificial intelligence. Focusing on the most cost-relevant phase of data preparation, the critical influences for the development of a maturity model for the phase of data preparation are identified and categorized. Additionally, a data preparation maturity model prototype is proposed. This provides the first step towards the design of a maturity model for data mining. Full article
(This article belongs to the Special Issue IT in Production and Logistics)
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18 pages, 571 KiB  
Article
Can ChatGPT Solve Undergraduate Exams from Warehousing Studies? An Investigation
by Sven Franke, Christoph Pott, Jérôme Rutinowski, Markus Pauly, Christopher Reining and Alice Kirchheim
Computers 2025, 14(2), 52; https://doi.org/10.3390/computers14020052 - 5 Feb 2025
Viewed by 764
Abstract
The performance of Large Language Models, such as ChatGPT, generally increases with every new model release. In this study, we investigated to what degree different GPT models were able to solve the exams of three different undergraduate courses on warehousing. We contribute to [...] Read more.
The performance of Large Language Models, such as ChatGPT, generally increases with every new model release. In this study, we investigated to what degree different GPT models were able to solve the exams of three different undergraduate courses on warehousing. We contribute to the discussion of ChatGPT’s existing logistics knowledge, particularly in the field of warehousing. Both the free version (GPT-4o mini) and the premium version (GPT-4o) completed three different warehousing exams using three different prompting techniques (with and without role assignments as logistics experts or students). The o1-preview model was also used (without a role assignment) for six runs. The tests were repeated three times. A total of 60 tests were conducted and compared with the in-class results of logistics students. The results show that the GPT models passed a total of 46 tests. The best run solved 93% of the exam correctly. Compared with the students from the respective semester, ChatGPT outperformed the students in one exam. In the other two exams, the students performed better on average than ChatGPT. Full article
(This article belongs to the Special Issue IT in Production and Logistics)
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16 pages, 902 KiB  
Article
Insights into How to Enhance Container Terminal Operations with Digital Twins
by Marvin Kastner, Nicolò Saporiti, Ann-Kathrin Lange and Tommaso Rossi
Computers 2024, 13(6), 138; https://doi.org/10.3390/computers13060138 - 30 May 2024
Cited by 2 | Viewed by 1623
Abstract
The years 2021 and 2022 showed that maritime logistics are prone to interruptions. Ports especially turned out to be bottlenecks with long queues of waiting vessels. This leads to the question of whether this can be (at least partly) mitigated by means of [...] Read more.
The years 2021 and 2022 showed that maritime logistics are prone to interruptions. Ports especially turned out to be bottlenecks with long queues of waiting vessels. This leads to the question of whether this can be (at least partly) mitigated by means of better and more flexible terminal operations. Digital Twins have been in use in production and logistics to increase flexibility in operations and to support operational decision-making based on real-time information. However, the true potential of Digital Twins to enhance terminal operations still needs to be further investigated. A Delphi study is conducted to explore the operational pain points, the best practices to counter them, and how these best practices can be supported by Digital Twins. A questionnaire with 16 propositions is developed, and a panel of 17 experts is asked for their degrees of confirmation for each. The results indicate that today’s terminal operations are far from ideal, and leave space for optimisation. The experts see great potential in analysing the past working shift data to identify the reasons for poor terminal performance. Moreover, they agree on the proposed best practices and support the use of emulation for detailed ad hoc simulation studies to improve operational decision-making. Full article
(This article belongs to the Special Issue IT in Production and Logistics)
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Review

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28 pages, 990 KiB  
Review
A Review of Vessel Time of Arrival Prediction on Waterway Networks: Current Trends, Open Issues, and Future Directions
by Abdullah Al Noman, Aaron Heuermann, Stefan Wiesner and Klaus-Dieter Thoben
Computers 2025, 14(2), 41; https://doi.org/10.3390/computers14020041 - 28 Jan 2025
Viewed by 947
Abstract
With the vast majority of global trade volume and value reliant on maritime transport, accurate prediction of vessel estimated time of arrival (ETA) is crucial for optimizing supply chain efficiency and managing logistical complexities in port operations. This review paper systematically examines the [...] Read more.
With the vast majority of global trade volume and value reliant on maritime transport, accurate prediction of vessel estimated time of arrival (ETA) is crucial for optimizing supply chain efficiency and managing logistical complexities in port operations. This review paper systematically examines the current state of research and practices in the field of vessel ETA prediction, highlighting significant trends, methodologies, and technologies. It explores various approaches, including classical methods, machine learning and deep learning algorithms, and hybrid methods, developed to enhance the accuracy and reliability of vessel travel time and arrival time predictions. Additionally, this paper categorizes key influencing factors and metrics, and identifies open issues and challenges within current prediction models. Concluding with proposed future research directions aimed at addressing the identified gaps and leveraging technological advancements, this review emphasizes the importance of fostering innovation in maritime ETA prediction systems, particularly within the framework of Intelligent Transportation Systems (ITSs) and maritime logistics. By applying a systematic literature review (SLR) methodology and conducting an in-depth evaluation, the results provide a comprehensive overview of vessel ETA prediction for researchers, practitioners, and policy makers involved in maritime transport and logistics, and offer insights into the potential for improved efficiency, safety, and environmental sustainability in waterway networks. Full article
(This article belongs to the Special Issue IT in Production and Logistics)
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