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Logistics and Supply Chain Challenges and Solutions in the Turbulent World

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A special issue of Logistics (ISSN 2305-6290). This special issue belongs to the section "Sustainable Supply Chains and Logistics".

Deadline for manuscript submissions: 30 April 2027 | Viewed by 14749

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Special Issue Editors

Dr. Edit Süle

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Guest Editor

Department of Corporate Leadership and Marketing, Széchenyi István University, 9026 Győr, Hungary
Interests: supply chain management; last-mile logistics; complex systems; sustainable ecosystems; Logistics 4.0; Industry 5.0

Dr. Diego D'Urso

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Guest Editor

Department of Electrical, Electronic and Computer Engineering, University of Catania, 95100 Catania, Italy
Interests: supply chain management; behavioral operations management; business process mapping and traceability; reliability modeling

Dr. Abderahman Rejeb

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Faculty of Business and Economics, Széchenyi István University, 9026 Győr, Hungary
Interests: Industry 5.0; human-centric digital transformation; blockchain technology; IoT; smart agriculture; sustainable supply chain management; green procurement; augmented reality; smart logistics; digital twins
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Special Issue Information

Dear Colleagues,

In today's turbulent global environment, logistics and supply chains face multifaceted challenges. The logistics sector is navigating a complex landscape characterized by geopolitical instability, trade wars, labor shortages, environmental concerns, natural, political, and economic crises, pandemics, and cybersecurity threats (Wiedmer, Griffis, 2021). Embracing digital transformation, investing in technology, and adopting automation are key strategies for building stable and transparent logistics processes and maintaining adaptable supply chains in this volatile era. Industry 5.0 (Kessler, Arlinghaus, 2022) represents a paradigm shift beyond the automation-first perspective of Industry 4.0 (Burger et al., 2021) by reintegrating humans, prioritizing sustainability, resilience, and personalization alongside digitalization. This theory of transition emphasizes human–machine collaboration in a new hybrid reality via the interconnection of the virtual and real worlds through the use of cobots, augmented reality, digital twins, and AI‑enhanced decision tools (Simon, 1995). Logistics 4.0 and Logistics 5.0 approaches and methods aim to reshape the traditional internal workflows, procurement and distribution strategies, and urban and global freight processes, before finally restructuring the entirety of logistical and supply chain operations.

These techniques have widespread applicability, finding use in the logistics domain—from procurement and inbound logistics, warehousing, inventory and operations management to distribution, reverse logistics, internal movements, transportation, city logistics, last-mile logistics, and global freight. These reshaping trends encourage the connection of technology, sustainability, and people to tackle disruptions and design more efficient, smarter, and more resilient logistics ecosystems.

We invite cutting‐edge papers using diverse methods for exploring solutions for these challenges including nature and physical systems’ analogies as a means for optimization (Sarraj et al., 2014; Sharma, Lote, 2013; Turken et al., 2020). All submissions should contribute to the body of scientific literature and practice primarily in logistics contexts, with insights for both academia and industry. Reflecting the evolution of logistics, we are particularly keen to publish contributions that integrate the following topics:

AI-powered workflows in logistics.
Sustainability targets across the supply chain.
Human-Centricity: Logistics 5.0 reintroduces the human role in decision-making and operations.
Resilience: Through adaptive supply chain design, real-time AI analytics, and human–machine synergy, logistics systems gain flexibility to handle disruptions.
Sustainability: Green logistics practices, optimized routing, energy‑efficient warehouses, and reverse logistics align with environmental goals.

Related literature:

Burger M., Kessler M., Arlinghaus J. (2021) Aiming for Industry 4.0 Maturity? The risk of higher digitalization levels in buyer-supplier relationships, Procedia CIRP, Volume 104, 1529-1534, https://doi.org/10.1016/j.procir.2021.11.258.

Kessler M., Arlinghaus J. C. (2022) A framework for human-centered production planning and control in smart manufacturing. Journal of Manufacturing Systems, Volume 65. 220-232, https://doi.org/10.1016/j.jmsy.2022.09.013.

Sarraj, R., Ballot, E., Pan, S. et al. Analogies between Internet network and logistics service networks: challenges involved in the interconnection. J Intell Manuf 25, 1207–1219 (2014). https://doi.org/10.1007/s10845-012-0697-7

Sharma, S., Lote, K.S. Understanding demand volatility in supply chains through the vibrations analogy - the onion supply case. Logist. Res. 6, 3–15 (2013). https://doi.org/10.1007/s12159-012-0083-z

Simon, H., A. (1995) ‘Artificial intelligence: an empirical science’, Artificial Intelligence, 77(1), pp. 95–127, https://doi.org/10.1016/0004-3702(95)00039-H.

Turken, N., Cannataro, V., Geda, A., & Dixit, A. (2020). Nature inspired supply chain solutions: definitions, analogies, and future research directions. International Journal of Production Research, 58(15), 4689–4715. https://doi.org/10.1080/00207543.2020.1778206

Wiedmer, R. & Griffis, SE (2021) Structural characteristics of complex supply chain networks, Journal of business logistics 42(2), https://doi.org/10.1111/jbl.12283

Dr. Edit Süle
Dr. Diego D'Urso
Dr. Abderahman Rejeb
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 250 words) can be sent to the Editorial Office for assessment.

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 double-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 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 1500 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

Industry 5.0
Industry 4.0
smart logistics
artificial intelligence
logistics management
supply chain management
analogy in logistics
IoT
digital twins
sustainable supply chain management
augmented reality

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Published Papers (8 papers)

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Research

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37 pages, 10157 KB

Open AccessArticle

Quantifying Transparency in Production Logistics: An Improved Process Modelling Technique for Supporting Digital Transformation

by Krisztián Bóna and Petra Molnár-Major

Logistics 2026, 10(4), 91; https://doi.org/10.3390/logistics10040091 - 14 Apr 2026

Viewed by 845

Abstract

Background: Production systems are complex environments where logistics processes play a crucial role alongside manufacturing. Although the digitalisation of value-creating processes is increasingly important, production-supporting logistics activities are often missing from digital models. Their absence reduces the accuracy of digital representations and may lead to suboptimal operational decisions. Methods: This study reviews digitalisation solutions in manufacturing systems with a focus on integrating production logistics activities. Relevant research articles are analysed, and integration problems are organised into a problem tree supported by practical experience. Based on these findings, an extended process modelling methodology and related indicators are applied to quantify digital transparency. The methodology is demonstrated through tests on a physical laboratory model. Results: The literature review and practical observations highlight several issues that hinder the integration and quantification of production logistics activities in digital models. The proposed modelling approach addresses these challenges by defining appropriate modelling depth and placement of logistics processes, enabling a clearer evaluation of digital transparency. Conclusions: Experiments conducted on the physical model confirm the feasibility of the methodology. The approach provides an important initial step toward the digital integration of production logistics and supports the development of more effective digital twin models for industrial applications in future research. Full article

(This article belongs to the Special Issue Logistics and Supply Chain Challenges and Solutions in the Turbulent World)

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37 pages, 2212 KB

Open AccessArticle

A Refined Kano Model Approach to Sustainable Last-Mile Convenience Services and Customer Satisfaction

by Balázs Gyenge, Viktor Póka and Kornélia Mészáros

Logistics 2026, 10(4), 86; https://doi.org/10.3390/logistics10040086 - 13 Apr 2026

Viewed by 678

Abstract

Background: Last-mile logistics is one of the most complex and cost-intensive segments of supply chains, particularly in densely populated urban environments where rising customer expectations, sustainability requirements, and operational constraints increasingly intersect. Despite growing academic interest, empirical evidence remains limited regarding how convenience-related last-mile service attributes influence customer satisfaction, while the sector is undergoing a revolutionary transformation. Methods: This study applies a refined Kano model to classify last-mile convenience services according to their differentiated effects on customer satisfaction. Data were collected through a structured questionnaire administered to active e-commerce users in a metropolitan area. The methodological approach modifies and extends the traditional Kano framework. Results: The findings reveal clear patterns among last-mile service attributes. Online tracking and preferred payment options function as One-dimensional attributes, proportionally influencing customer satisfaction. Time-based delivery, flexible pickup options, and sustainability-oriented service features appear as Attractive attributes, generating additional increases in service value. In contrast, advanced technological solutions such as drone or autonomous vehicle delivery were perceived as Indifferent attributes. These interpretations are further nuanced by the fuzzy approach. Conclusions: The results provide important insights and validation for consumer-centered service design and support the prioritization of investments aimed at developing sustainable and customer-oriented last-mile logistics systems. Full article

(This article belongs to the Special Issue Logistics and Supply Chain Challenges and Solutions in the Turbulent World)

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21 pages, 1930 KB

Open AccessArticle

Targeting Toward Optimal Inventory in Automotive Industry—An Analysis Based on Six Sigma Methodology

by Ionela-Roxana Puiu, Ioana Mădălina Petre and Mircea Boșcoianu

Logistics 2026, 10(1), 8; https://doi.org/10.3390/logistics10010008 - 27 Dec 2025

Viewed by 1331

Abstract

Background: This paper presents an analysis and a structured framework for improving inventory accuracy in an automotive factory, considering the current context of global disruptions. In 2023, the company recorded 20,340 inventory adjustments (1695 per month) and a 0.24% monthly net value discrepancy (EUR 256,594 YTD), with a baseline absolute discrepancy of 2.21% of sales. The project aimed to reduce adjustments to below 700 per month and the net value discrepancy to 0.1%. Methods: The research followed the Six Sigma methodology’s Define, Measure, Analyze, Improve and Control (DMAIC) phases, integrating Root Cause Analysis (RCA) and Failure Mode and Effects Analysis (FMEA) to enhance inventory accuracy in manufacturing operations. Results: Implementation significantly improved inventory accuracy: monthly adjustments decreased from 1695 to 971, the highest RPN was reduced from 576 to 144, and the absolute discrepancy-to-sales ratio stabilized at 0.98% (a 56% improvement). Financial variance was reduced to EUR 1948.10 in Q4 2024, while organizational discipline, role clarity and process control also increased. Conclusions: The integrated DMAIC–RCA–FMEA framework proved effective and replicable, enabling systematic identification of root causes, targeted corrective actions and sustainable KPI-driven improvements. The results demonstrate a scalable approach to inventory optimization that supports operational resilience and supply chain performance. Full article

(This article belongs to the Special Issue Logistics and Supply Chain Challenges and Solutions in the Turbulent World)

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25 pages, 1881 KB

Open AccessArticle

RFID-Enhanced Modified Two-Bin System for Reducing Excess Inventory of FMCG Industry

by Shuvojit Das, Gazi Md. Mahbubul Alam Rajin, Md. Nazmul Hasan Sarker, Md. Mahraj Uddin, Golam Sakaline and Edit Süle

Logistics 2025, 9(4), 167; https://doi.org/10.3390/logistics9040167 - 24 Nov 2025

Viewed by 1698

Abstract

Background: Globally, in the Fast-Moving Consumer Goods (FMCG) industry, excess inventory results from the bullwhip effect. Earlier, barcode-based two-bin systems were limited by manual scanning; hence, a more responsive system is needed to align the inventory with real-time demand. Prior studies have predominantly concentrated on mitigating demand fluctuations and employed comparatively low-efficiency systems, hindering excess inventory (EI) reduction. Methods: This study proposes identifying research gaps, considering the distributor-manufacturer relationship, and developing an RFID-based modified two-bin system and mathematical model to reduce EI and control over manufacturers’ excessive cost. Results: This study tested through Python-based simulation using historical data from an FMCG manufacturer, and the proposed model achieved a reduction in 67% EI and 73% month-wise holding costs. Moreover, the integration of the Artificial Bee Colony algorithm optimizes rework rates within budget, including reworking shop-floor and holding costs, contributing to a monthly excessive cost reduction of 34–48%, alongside a corresponding 41–44% cumulative excessive cost reduction. Conclusions: Bringing significant implications on digitalized SCM, this study offers a practical and scalable solution for perishable FMCG items facing demand variability and budget constraints. Collectively, this novel perspective bridges research gaps and motivates future research for embedding trend-aligned parameters, enhancing the model’s performance through diverse SCM contexts like safety stock and backorder cost optimization. Full article

(This article belongs to the Special Issue Logistics and Supply Chain Challenges and Solutions in the Turbulent World)

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17 pages, 1626 KB

Open AccessArticle

Modal Distribution Diversification and Intermodal Transport Analysis in Europe: A Comprehensive Investigation of Freight Transport Patterns

by Ana Castro, Gabriel Ludke, Vânia Dias, Sónia Longras, Edit Sule, Estela Vilhena and António Rocha

Logistics 2025, 9(4), 162; https://doi.org/10.3390/logistics9040162 - 18 Nov 2025

Viewed by 2321

Abstract

Background: This study analyzes modal distribution patterns across Europe and 28 European countries, employing clustering analysis to identify trends in transport mode utilization. The research quantifies logistics diversification, examining extreme cases and addressing gaps in understanding modal transitions affecting environmental and economic performance. Methods: Statistical testing using compositional data transformations revealed significant differences between modal distributions (p < 0.001), justifying country-specific and European-level assessments. K-means clustering was applied to identify groups of countries with similar modal distribution patterns. Results: Maritime and road transport constitute the predominant modes across all analyzed countries in the study period. Among terrestrial modes, road transport dominates universally, exhibiting systematic growth, while rail transport experienced a corresponding decline. This trend directly contradicts European sustainability objectives promoting modal shift toward environmentally superior alternatives. Romania demonstrates the highest logistics diversification with the most balanced modal distribution, while Portugal exhibits the lowest diversification due to maritime transport dominance. K-means clustering positioned Portugal within a maritime-dominated group alongside Greece, Cyprus, and Ireland, reflecting similar geographical constraints and distribution patterns. Conclusions: The findings reveal critical aspects requiring further investigation concerning European modal distribution trends that challenge current policy effectiveness, highlighting the divergence between observed transport patterns and stated sustainability goals. These results provide essential insights for addressing persistent modal shift challenges in European transport systems. Full article

(This article belongs to the Special Issue Logistics and Supply Chain Challenges and Solutions in the Turbulent World)

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28 pages, 1539 KB

Open AccessArticle

Optimal Inventory Planning at the Retail Level, in a Multi-Product Environment, Enabled with Stochastic Demand and Deterministic Lead Time

by Andrés Julián Barrera-Sánchez and Rafael Guillermo García-Cáceres

Logistics 2025, 9(3), 128; https://doi.org/10.3390/logistics9030128 - 11 Sep 2025

Cited by 1 | Viewed by 4571

Abstract

Background: Inventory planning in retail supply chains requires balancing cost efficiency and service reliability under demand uncertainty and financial limitations. The literature has seldom addressed the joint integration of stochastic demand, deterministic lead times, and supplier-specific constraints in multi-product and multi-warehouse settings, particularly in the context of small- and medium-sized enterprises. Methods: This study develops a Stochastic Pure Integer Linear Programming (SPILP) model that incorporates stochastic demand, deterministic lead times, budget ceilings, and trade credit conditions across multiple suppliers and warehouses. A two-step solution procedure is proposed, combining a chance-constrained approach to manage uncertainty with warm-start heuristics and relaxation-based preprocessing to improve computational efficiency. Results: Model validation using data from a Colombian retail distributor showed cost reductions of up to 17% (average 15%) while maintaining or improving service levels. Computational experiments confirmed scalability, solving instances with more than 574,000 variables in less than 8800 s. Sensitivity analyses revealed nonlinear trade-offs between service levels and planning horizons, showing that very high service levels or short planning periods substantially increase costs. Conclusions: The findings demonstrate that the proposed model provides an effective decision support system for inventory planning under uncertainty, offering robust, scalable, and practical solutions that integrate operational and financial constraints for medium-sized retailers. Full article

(This article belongs to the Special Issue Logistics and Supply Chain Challenges and Solutions in the Turbulent World)

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37 pages, 2933 KB

Open AccessSystematic Review

Inbound Logistics Optimization Under Uncertainty: Systematic Literature Review

by Celeste Gaxiola-Goray, Luis Alberto Rodríguez-Picón and Víctor Hugo Flores-Ochoa

Logistics 2026, 10(4), 82; https://doi.org/10.3390/logistics10040082 - 3 Apr 2026

Viewed by 957

Abstract

Background: Inbound logistics (IL) is a critical subsystem of the supply chain (SC) that supports production destined for the end consumer. Its effectiveness is reduced by uncertainty, which generates inaccuracies in production planning, disruptions, bottlenecks, and waste. Methods: This article presents a systematic review to identify key concepts, variables, and optimization methodologies for IL under conditions of uncertainty. The PRISMA methodology and two article evaluation tools were applied. These methodologies allowed for the identification of 26,555 documents before applying inclusion and exclusion filters. After applying the selection criteria, the analysis concludes with the analysis of 39 articles that stood out for their empirical relevance and methodological soundness. Results: This study makes a theoretical contribution by integrating IL variables, optimization methods, and uncertainty within a structured framework. Conclusions: In practice, it facilitates decision-making by identifying key variables and approaches for designing more robust logistics systems in uncertain environments. Furthermore, the possibility of generating new research focused on optimization under conditions of uncertainty is recognized through the proposal of hybrid optimization models that integrate input variables from IL and formal methods to address uncertainty. Full article

(This article belongs to the Special Issue Logistics and Supply Chain Challenges and Solutions in the Turbulent World)

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23 pages, 908 KB

Open AccessSystematic Review

Traditional and Innovative Managerial Adaptations in Dairy Supply Chains During COVID-19: A Comprehensive Review

by Fachri Rizky Sitompul and Csaba Borbély

Logistics 2026, 10(3), 58; https://doi.org/10.3390/logistics10030058 - 9 Mar 2026

Viewed by 798

Abstract

Background: The COVID-19 pandemic disrupted global dairy supply chains and threatened business continuity from farms to retail outlets. There is limited understanding of how operational-level managerial decisions supported resilience in this perishable sector. Methods: This study applies a systematic literature review based on PRISMA 2020 guidelines. It analyses 21 peer-reviewed studies published between 2019 and 2025 across 19 countries. Results: The findings identify 8 primary supply chain challenges. Adaptive responses are classified into traditional and innovative managerial adaptations. Traditional adaptations rely on established practices such as production adjustments, cross-training, and product reallocation to stabilise short-term performance. Innovative adaptations involve structural and analytical approaches such as network optimisation, digital coordination, and scenario planning to support long-term resilience. The results also reveal differences between developed and developing economies. Conclusions: Resilient dairy supply chains require both operational continuity and structural innovation. This study proposes a sector-specific classification of managerial adaptations and highlights directions for future research. Full article

(This article belongs to the Special Issue Logistics and Supply Chain Challenges and Solutions in the Turbulent World)

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