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Logistics
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Smart, Agile, Sustainable & Integrated: The Logistics of the Future
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Smart, Agile, Sustainable & Integrated: The Logistics of the Future

A special issue of Logistics (ISSN 2305-6290).

Deadline for manuscript submissions: closed (1 August 2024) | Viewed by 24343

Special Issue Editors

Dr. Vasilis Zeimpekis

E-Mail Website
Guest Editor
Department of Financial & Management Engineering, School of Engineering, University of the Aegean, 82100 Chios, Greece
Interests: freight transportation; city logistics; last-mile delivery; warehouse optimization; logistics 4.0; digital twin; logistics information systems; sustainable logistics
Special Issues, Collections and Topics in MDPI journals
Dr. Michael A. Madas

E-Mail Website
Guest Editor
Department of Applied Informatics, School of Information Sciences, Information Systems and e-Business Laboratory (ISeB), University of Macedonia, 54636 Thessaloniki, Greece
Interests: transportation; terminal operations; city logistics; sustainable logistics; operational research applications in transport and logistics; emerging technologies and ICT applications in logistics and supply chain management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last few years, the complexity of logistics operations has grown significantly due to the increase in e-commerce sales, customer requests for frequent and omni-channel distribution, and the need for faster response times. Concepts such as Industry 4.0 and smart logistics have induced enterprises to deeply integrate logistics with information systems, new business models, and automation, hence gradually shifting towards more efficient, intelligent, and humanized supply chain ecosystems.

This Special Issue invites papers from experienced scholars and practitioners that systematically address the development of logistics operations and put forward constructive research topics and views. In particular, we seek contributions on applied business research methods, ranging from qualitative to quantitative and hybrid/mixed methods, that advance the theory and practice of the following areas and beyond:

  • Agile and smart logistics operations;
  • Warehouse optimization;
  • Order-picking technologies;
  • Warehouse robotics and automation;
  • Digital twin in logistics operations;
  • Logistics 4.0 technologies (e.g., IoT, machine learning, artificial intelligence, AGVs, drones, AMRs);
  • Logistics information systems;
  • City logistics and urban consolidation centers;
  • Shared logistics;
  • Vehicle routing and scheduling methods and systems;
  • Last-mile orchestration;
  • Omni-channel distribution models and technologies;
  • Reverse logistics;
  • Green logistics;
  • Sustainable transport;
  • Smart and sustainable transport terminals.

We look forward to receiving your contributions.

Dr. Vasilis Zeimpekis
Dr. Michael A. Madas
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. Logistics is an international peer-reviewed open access quarterly 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 1400 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

  • smart logistics
  • logistics 4.0 technologies
  • logistics information systems
  • warehouse technologies and systems
  • sustainable logistics
  • city logistics
  • freight transportation and terminal operations
  • fleet routing and management
  • omni- channel distribution

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

Published Papers (5 papers)

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Research

21 pages, 1300 KiB  
Article
Modeling User Intentions for Electric Vehicle Adoption in Thailand: Incorporating Multilayer Preference Heterogeneity
by Thanapong Champahom, Chamroeun Se, Wimon Laphrom, Sajjakaj Jomnonkwao, Ampol Karoonsoontawong and Vatanavongs Ratanavaraha
Logistics 2024, 8(3), 83; https://doi.org/10.3390/logistics8030083 - 19 Aug 2024
Viewed by 1252
Abstract
Background: The automotive industry is pivotal in advancing sustainability, with electric vehicles (EVs) essential for reducing emissions and promoting cleaner transport. This study examines the determinants of EV adoption intentions in Thailand, integrating demographic and psychographic factors from Environmental psychology and innovation [...] Read more.
Background: The automotive industry is pivotal in advancing sustainability, with electric vehicles (EVs) essential for reducing emissions and promoting cleaner transport. This study examines the determinants of EV adoption intentions in Thailand, integrating demographic and psychographic factors from Environmental psychology and innovation diffusion theory; Methods: Data from a structured questionnaire, administered to 4003 respondents at gas stations with EV charging facilities across Thailand, were analyzed using a Correlated Mixed-Ordered Probit Model with Heterogeneity in Means (CMOPMHM); Results: Findings indicate that younger adults, particularly those aged 25–34 years old and 45–54 years old, are more likely to adopt EVs, whereas conventional or hybrid vehicle owners are less inclined. Rural residency or travel also hinders adoption. Individuals with strong environmental values and openness to new technologies are more likely to adopt EVs; Conclusions: The proposed model quantified the relative importance of these factors and uncovered heterogeneity in user preferences, offering reliable and valuable insights for policymakers, EV manufacturers, and researchers. The study suggests targeted policies and enhanced charging infrastructure, especially in rural areas, and recommends leveraging environmental values and trialability through communication campaigns and test drive events. These insights can guide the development of targeted incentives, infrastructure expansion, communication strategies, and trialability programs to effectively promote wider EV adoption in Thailand and similar markets. Full article
(This article belongs to the Special Issue Smart, Agile, Sustainable & Integrated: The Logistics of the Future)
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16 pages, 246 KiB  
Article
Enhancing Supply Chain Agility and Sustainability through Machine Learning: Optimization Techniques for Logistics and Inventory Management
by Vikram Pasupuleti, Bharadwaj Thuraka, Chandra Shikhi Kodete and Saiteja Malisetty
Logistics 2024, 8(3), 73; https://doi.org/10.3390/logistics8030073 - 17 Jul 2024
Cited by 12 | Viewed by 11974
Abstract
Background: In the current global market, supply chains are increasingly complex, necessitating agile and sustainable management strategies. Traditional analytical methods often fall short in addressing these challenges, creating a need for more advanced approaches. Methods: This study leverages advanced machine learning [...] Read more.
Background: In the current global market, supply chains are increasingly complex, necessitating agile and sustainable management strategies. Traditional analytical methods often fall short in addressing these challenges, creating a need for more advanced approaches. Methods: This study leverages advanced machine learning (ML) techniques to enhance logistics and inventory man-agement. Using historical data from a multinational retail corporation, including sales, inventory levels, order fulfillment rates, and operational costs, we applied a variety of ML algorithms, in-cluding regression, classification, clustering, and time series analysis. Results: The application of these ML models resulted in significant improvements across key operational areas. We achieved a 15% increase in demand forecasting accuracy, a 10% reduction in overstock and stockouts, and a 95% accuracy in predicting order fulfillment timelines. Additionally, the approach identified at-risk shipments and enabled customer segmentation based on delivery preferences, leading to more personalized service offerings. Conclusions: Our evaluation demonstrates the transforma-tive potential of ML in making supply chain operations more responsive and data-driven. The study underscores the importance of adopting advanced technologies to enhance deci-sion-making, evidenced by a 12% improvement in lead time efficiency, a silhouette coefficient of 0.75 for clustering, and an 8% reduction in replenishment errors. Full article
(This article belongs to the Special Issue Smart, Agile, Sustainable & Integrated: The Logistics of the Future)
21 pages, 1753 KiB  
Article
Exploring the Implementation Challenges of the Electronic Freight Transport Information (eFTI) Regulation: An Empirical Perspective from Greece
by Thomas K. Dasaklis, Evangelia Kopanaki, Panos T. Chountalas, Nikolaos P. Rachaniotis, Theodore G. Voutsinas, Kyriakos Giannakis and Gregory Chondrokoukis
Logistics 2024, 8(1), 30; https://doi.org/10.3390/logistics8010030 - 15 Mar 2024
Viewed by 1931
Abstract
Background: The electronic Freight Transport Information (eFTI) regulation is critical in modernizing freight transport (FT) within the European Union by establishing a framework for the electronic exchange of information. Despite its importance, there is a notable gap in the literature regarding the [...] Read more.
Background: The electronic Freight Transport Information (eFTI) regulation is critical in modernizing freight transport (FT) within the European Union by establishing a framework for the electronic exchange of information. Despite its importance, there is a notable gap in the literature regarding the practical implementation challenges, especially from an empirical perspective. Methods: To address this gap, our study utilized a grounded theory approach, conducting interviews with a diverse group of logistics experts from Greece. The selection of experts was strategic to ensure a comprehensive range of knowledge and expertise, including insights at the policy level as well as practical experiences. Results: Our findings highlight several significant challenges in the implementation of eFTI, including the digital skill gap among the workforce, issues with system interoperability, and diverse capacities and resources of companies of different sizes. Economic factors, regulatory frameworks and the necessity for targeted training and leadership support were also identified as crucial for the digital transition. Conclusions: The study shows that uniform eFTI implementation may not work for all organizations, highlighting the necessity for customized strategies that address specific challenges in the FT chain. Our research deepens the understanding of these issues, providing actionable insights for successful eFTI adoption. Full article
(This article belongs to the Special Issue Smart, Agile, Sustainable & Integrated: The Logistics of the Future)
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15 pages, 3151 KiB  
Article
Investigating the Impact of Completion Time and Perceived Workload in Pickers-to-Parts Order-Picking Technologies: Evidence from Laboratory Experiments
by Nikolaos Chondromatidis, Anastasios Gialos, Vasileios Zeimpekis and Michael Madas
Logistics 2024, 8(1), 13; https://doi.org/10.3390/logistics8010013 - 30 Jan 2024
Cited by 1 | Viewed by 2053
Abstract
Background: Despite the general impression that digital order-picking supportive technologies can manage a series of emerging challenges, there is still a very limited amount of research concerning the implementation and evaluation of such technologies in manual picker-to-goods order-picking systems. Therefore, this paper aims [...] Read more.
Background: Despite the general impression that digital order-picking supportive technologies can manage a series of emerging challenges, there is still a very limited amount of research concerning the implementation and evaluation of such technologies in manual picker-to-goods order-picking systems. Therefore, this paper aims to evaluate the performance of three alternative picker-to-goods technologies (i.e., Pick-by-Radio Frequency (RF) Scanner, Pick-to-light, and Pick-by-vision) in terms of completion time and perceived workload. Methods: The Design of Experiments (DoE) methodology is adopted to investigate order-picking technologies in terms of completion time. More specifically, a full factorial design has been used (23 × 3 full factorial design) for the assessment of the aforementioned order-picking technologies via laboratory testing. Furthermore, for the comparative assessment of the reviewed order-picking technologies in terms of workload, the NASA Task Load Index (NASA-TLX) is embraced by system users. Results: The results reveal that the best picker-to-goods technology in terms of order-picking completion time and perceived workload under certain laboratory setup is light picking when combined with few items per order line and many order lines per order. Conclusion: The paper successfully identified the best picker-to-goods technology, however it is important to mention that the adoption of such order-picking technology implies certain managerial implications that include training programs for employees to ensure they are proficient in using such technologies, upfront costs for purchasing and implementing the order picking system, and adjustments to existing workflows. Full article
(This article belongs to the Special Issue Smart, Agile, Sustainable & Integrated: The Logistics of the Future)
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35 pages, 1305 KiB  
Article
Measuring Supply Chain Performance as SCOR v13.0-Based in Disruptive Technology Era: Scale Development and Validation
by Özden Özkanlısoy and Füsun Bulutlar
Logistics 2023, 7(3), 65; https://doi.org/10.3390/logistics7030065 - 18 Sep 2023
Cited by 4 | Viewed by 5850
Abstract
Background: Supply chain performance measurement is an integral part of supply chain management today, as it makes many critical contributions to supply chains, especially for companies and supply chains to identify potential problems and improvement fields, evaluate the efficiency of processes, and enhance [...] Read more.
Background: Supply chain performance measurement is an integral part of supply chain management today, as it makes many critical contributions to supply chains, especially for companies and supply chains to identify potential problems and improvement fields, evaluate the efficiency of processes, and enhance the health and success of supply chains. The purpose of this study is to contribute to future research and practical applications by presenting a more standard, comprehensive, and up-to-date measurement scale developed based on the SCOR model version 13.0 performance measures in the disruptive technology era. Methods: The study was performed in seven stages and the sample size consists of 227 companies for pilot data and 452 companies for the main data. The stages comprise item generation and purification, exploratory factor analysis for the pilot study and main study, confirmatory factor analysis for the main study, convergent, discriminant, and nomological validity appraisal, and investigation of bias effect. Results: The scale was developed and validated as a five-factor and thirty-one item structure. Conclusions: Some key trends and indicators must be followed today to perceive the landscape of future supply chains. This measurement scale closely follows the future supply chains. Additionally, the findings have been confirmed by the contributions of disruptive technologies and the conceptual structure of supply chain management. Full article
(This article belongs to the Special Issue Smart, Agile, Sustainable & Integrated: The Logistics of the Future)
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