Supply Chain Management (SCM) provides an integrated, cross-organizational perspective on all business processes, encompassing key areas such as procurement, production, distribution, marketing, and controlling. These areas are crucial to the value chain, directly influencing efficiency and competitiveness. Optimizing them is essential for long-term business success. In this context, machine learning (ML) and deep learning (DL)-based artificial intelligence (AI) models offer advanced tools for enhancing these processes through data analysis and processing. Although advanced AI technologies and algorithms have become theoretically accessible to enterprises of all sizes through open-source software, the practical implementation of these sophisticated models remains a significant challenge.

There is a significant research gap in the area of practical applications of AI models in SCM that complicates a clear understanding of specific use cases. This paper provides an overview of current and potential AI techniques, focusing on experimental studies, case studies, and practical problem-solving approaches that can enhance SCM.

A Multivocal Literature Review was conducted, incorporating formal literature from databases such as ScienceDirect and Scopus, as well as grey literature from sources like Google. The search process followed an adapted approach based on Denyer and Tranfield (2009) and integrated elements of the PRISMA framework (Page et al., 2021) to ensure quality and transparency. Identification of grey literature followed the method pruposed by Garousi et al. (2018). The Google search was restricted to PDF files, and the top 200 search results were reviewed. In total, 1,454 reports were examined, with 121 included in the qualitative analysis.

The results reveal that the most frequently employed AI technique is from the field of deep learning, specifically artificial neural networks in various forms (FFNN, CNN, RNN), which were utilized in 71% of the reviewed literature and applied across all investigated SCM domains. These deep learning approaches significantly contribute to quality management, pattern recognition, reliability analysis, demand forecasting, fault diagnosis, and improvements in risk management and resilience.

This study provides important insights into the challenges and motivation in AI adoption. A central challenge, particularly within SCM environments, is the necessary collaboration among multiple stakeholders and the management of heterogeneous data sources. Additional challenges include trust in AI outcomes, concerns about data security, issues in change management, and the lack of suitable frameworks for AI adoption and governance. The motivations for AI implementation are diverse, with performance optimization being a primary focus. Additionally, ecological motivations, such as promoting sustainability in production, and enhancing resilience are also noted.

This paper provides a novel perspective on SCM and emphasizes the critical role of AI.

Global manufacturing companies need to incorporate environmental, social, and economic criteria to enhance their competitiveness and sustainability. These metrics are becoming increasingly important. The movement of commodities, the provision of services, and logistical processes are all areas in which supply chain operations have a substantial impact on the environment. Important strategic decisions, particularly in carrier and mode of transportation selection, significantly impact the efficiency of logistics. Beneficial Cargo Owners (BCOs), or the companies whose goods are being delivered, must carefully examine several variables when choosing a carrier, including cargo security, equipment availability, transit times, and freight costs. These decisions are crucial because they have an immediate impact on customer satisfaction and operational effectiveness. To track cargo and manage inventory, BCOs mostly rely on real-time data. This allows them to guarantee accurate and timely deliveries, which improves the overall customer experience and satisfaction levels. In response to global imperatives and stakeholder expectations, manufacturing giants are increasingly acting upon sustainability mandates reflected in their reports. These actions include managing shipment volumes, adopting alternative energy sources, and actively pursuing strategies to mitigate emissions across their supply chains. The quest for more effective strategies at the strategic level is driving BCOs to embrace innovative approaches such as the implementation of multimodal freight transportation models. Through technology integration, service quality, and environmental impact factors, carrier selection criteria are optimized in this manner. The goal of the approach is to help BCOs achieve more sustainability and efficiency in their supply chain operations by emphasizing the relationship between technology, service quality, and environmental responsibility. They can minimize their environmental impact while optimizing operations through strategic decisions in carrier selection and logistics. Companies that adopt such models are positioned strategically to satisfy changing customer expectations for environmentally friendly operations and to comply with industry standards and regulatory regulations. Examining these important parameters in detail, this study suggests an all-encompassing, sustainable multimodal freight transportation optimization model. Two integrated decision support mechanisms are included in the model: a route generation mechanism that generates different routes based on the limitations of the BCO and a multi-criteria decision-making system that chooses the most appropriate carrier. At this stage, a mixed-integer programming model is created to produce multimodal transportation routes after a multi-criteria decision-making analysis is completed on the established carrier selection criteria. Based on the outcomes of the multi-criteria decision-making study, the model's parameters and decision variables are assigned. As a future direction of the study, an application phase using case study data will be carried out to validate the generic model that is intended to be fitted to operating BCOs in various sectors. As a result, this study positions itself to provide a way forward for improved operational effectiveness, lower expenses, and more customer satisfaction in the ever-changing world of international trade.

During outbound rail transportation from seaport terminals, containers are secured to the rail wagons using foldable pins to prevent slipping. In seaport terminals, depending on the container train loading scheme, the container pins must be flipped up or down before commencing container loading operations. The research project "Pin-Handling-mR" aims to automate this process using a mobile robot. The automation is expected to enhance the safety of terminal staff and reduce operational costs for terminal operators. A key challenge to enable this automation is the accurate recognition and pixel wise segmentation of container pins, that is essential for precise robotic manipulation. Deep learning frameworks can be leveraged to accurately detect, classify and segment container pins. However, such frameworks require high-quality annotated data for training, and to the best of author’s knowledge, no publicly sourced container-pin dataset exists to train and test a deep learning image segmentation network for real time container pin segmentation.

This study focuses on the evaluation of the state-of-the-art deep learning based models to detect and segment the pins with better accuracy. It proposes the creation of a Red-Green-Blue-Depth (RGB-D) container pin dataset, featuring two types of container pins, and the development of a deep learning-based object segmentation pipeline. The dataset is generated using images from two sources – first, a demonstrator table equipped with container pins and an RGB-D camera and second, images captured from the Container Terminal Tollerort (CTT) in Hamburg, Germany. Two state-of-the-art deep learning networks, namely Mask-RCNN and CMX, are trained by fusion of RGB and depth data from dataset and fine-tuned using a transfer learning approach. The performance of these networks is compared using evaluation metrics for both RGB and RGB-D data. Validation and final tests are performed through segmentation tests on images collected from CTT. The final trained networks achieve accurate classification and generate precise pixel-level category masks for both RGB and depth modalities.

The results demonstrate that the best-performing network has achieved an Average Precision (AP) of 92.5%, an Average Recall (AR) of 94%, and category-wise APs of 92.3%, 88.29%, and 97.1%. Such successful inference confirms the viability of deep learning-based frameworks for container-pin segmentation tasks.

Objective: propose and test a methodology for assessing supply chain resilience requirements in a company. The methodology is composed by three logical steps: (i) the assessment of the current state of the supply chain resilience capabilities, (ii) the identification of the prioritized capabilities and (iii) the comparison between the current state and the prioritized capabilities to identify supply chain resilience requirements and directions for actions.

Methodology: based on the literature review, four main categories of risk sources and six supply chain resilience capabilities are identified and described through their enablers. The proposed approach is validated using a two-step methodology. The unit of analysis is a company, and the methodologies have been applied to three manufacturing companies. First, interviews with managers were conducted to understand their supply chain configuration, the risk sources they have to manage, as well as how they prevent, respond and react to these potential disruptions. Overall, seven interviews were conducted with managers of different functions to have a broader perspective of disruptions and resilience responses. Based on the interviews and the initial mapping of capabilities and disruptions, a workshop was conducted with other stakeholders within the case studies (about 2 people per company), with the objective of proposing a risk-capability prioritisation map.

Initial findings: all the companies are experiencing a wide variety of disruptions, both internal and external to the company. Among the most recurring and relevant in terms of impact severity and frequency, companies mentioned Availability of supplies, Internal disruptions due to facility breakdown, Natural disasters, Epidemics and pandemics, and Geopolitical issues. A widespread recognition and knowledge of the risks threatening the supply chain of case studies emerged. The companies have mentioned they use a wide range of enablers, which support all the identified capabilities. They mentioned Awareness and Alertness, Velocity and Flexibility as essential to tackling all the most relevant risk sources. Other capabilities are needed to tackle specific risk sources, for instance, Visibility is supportive when suppliers’ lead time are longer than expected. While Collaboration is helpful in the case of Environmental risks such as Natural disasters and Epidemics and pandemics, as well as for Internal risks such as Availability of supplies and Facility breakdowns.

Practical and theoretical implications: this research, firstly, contributes to the resilience body of knowledge by discussing the role of certain capabilities in reducing disruption propagation. Secondly, by merging the literature review and empirical results we propose a methodology for companies to assess supply chain resilience capabilities against disruptions. Moreover, bridging the gap between theoretical resilience research and practical application with real-world case studies opens the door for further studies and generalization across different industries.

Understanding potential risks enables companies to implement proactive measures to mitigate their impacts during emergencies, thereby enhancing the resilience of their supply chains. This paper aims to develop a comprehensive framework for the risk assessment of suppliers, enabling the identification of existing supplier-related risks. By fostering risk awareness, companies can implement effective measures to accelerate the return to their original state following a disruption.

Given disruptions such as the Suez Canal blockage, the global economic impacts of a pandemic, or a war, the necessity of integrating resilience into corporate strategies is more evident than ever. Supply chain resilience can be defined as a supply chains ability to quickly return to its original state or a new, more preferable state after a disruption. In the dynamic field of supply chain management, maintaining resilience against disruptions is crucial for the integrity and performance of supply chains. Technological advancements, rising customer demands, and intense competition have further increased the complexity and interdependence of global supply chains. Despite these challenges, supplier selection criteria primarily focus on delivery reliability, quality, and cost.

Supply networks consist of various interconnected actors. To strengthen the resilience of an entire supply network, it is initially advisable to enhance the resilience at individual nodes. For this purpose, the risk assessment of individual actors within a supply network is crucial, especially from a customer’s perspective in evaluating the risks associated with their suppliers. This external risk assessment allows the customer to identify the risks posed by their suppliers and to preemptively enhance robustness at the more vulnerable nodes.

Supply Chain Risk Management has garnered significant attention from experts and academics worldwide. However, there is often an absence of clear differentiation between risk dimensions and factors, complicating a uniform understanding of key risk aspects. This ambiguity leads to varying interpretations and, consequently, different levels of analysis. This paper presents a comprehensive framework for the analysis and assessment of supplier risks.

We identify a range of risk factors associated with suppliers and develop a multidimensional assessment framework that categorizes suppliers according to their risk levels. Additionally, we provide a comprehensive mapping of information sources relevant to the identification of supplier risks. Our approach is based on the external customer perspective, assessing the potential failure risk of a supplier, considering the availability of information from an external viewpoint. The framework is derived from a variety of theoretical factors but is designed with a focus on practical applicability. It is thus not merely a theoretical construct but an operational tool for evaluating actors within an entire supply network. By identifying potential risks among the actors, companies can develop detailed response strategies that can be quickly implemented in case of a disruption. In this way, a company can shorten the duration of disruptions, as measures exist to restore the original state as quickly as possible. Our findings underscore the critical importance of integrating risk analysis into supplier management processes, as this can be used as a lever to improve the overall resilience of a supply chain.

This study examines current practices in the horticultural supply chain to inform future scenarios, with a focus on supply chain coordination. The study was motivated by the limited research linking the rural horticulture supply chain, post-harvest losses,and agricultural practices. The main objective was to identify the practices in the supply chain that contribute to post-harvest losses and to propose strategies to reduce these losses. The data collection took place in two phases. The first phase comprised six focus group discussions (FGDs) with smallholder farmers, policymakers, extension staff, and representatives of non-governmental organizations (NGOs), financial institutions, and civil society organizations (CSOs) in the Arusha and Kilimanjaro regions. Additional insights were gained through interviews with key informants from the transport sector, entrepreneurs, and agricultural offices, in which a total of 61 people participated. In the second phase, 221 people involved in the horticultural supply chain in these regions were interviewed. Data analysis for the first phase followed the principles of explanatory inference to account for the complex dynamics of the horticultural supply chain, while structural equation modeling was used in the second phase. The results show that the performance of the rural horticulture supply chain is affected by several factors, including lack of coordination, poor information sharing, and inadequate communication. The presence of multiple actors using different and poor means of transport such as oxen, auto-rickshaws, motorbikes, and pickups— leads to multiple handling before the produce reaches the market. In addition, lack of information on commodity movement, asynchronous harvesting practices, poor demand forecasting, poor production planning, inefficient harvesting, and inappropriate packaging complicate the logistical challenges and hinder decision-making by smallholder farmers. The lack of suitable warehouses within the supply chain increases the risk of post-harvest losses due to inadequate storage facilities, especially in rural areas. The combination of a fragmented supply chain, transport problems, and inadequate infrastructure significantly exacerbates post-harvest losses, which account for 40% of the harvest and result in a lower market value. Given the critical role that transporters play in maintaining the freshness and nutritional value of perishable produce, there is an urgent need to prioritize efficient logistics and robust transport networks, especially in rural areas. Future strategies should include the establishment of logistics hubs, digitization and information points that facilitate real-time data integration, delivery routes, performance monitoring and information sharing. This should go hand in hand with well-planned production and coordinated harvesting. Appropriate handling and transport means to optimize the supply chain and reduce post-harvest losses are important. This study offers new insights into the challenges faced by rural smallholders in the horticultural supply chain and provides practical recommendations to improve resilience. Its focus on coordination, information access and infrastructure development sets it apart from other studies in this area. The findings are broadly applicable to rural horticultural supply chains in less developed countries.

Purpose

Against the backdrop of a tensioned geopolitical landscape in Europe, the concept of logistics preparedness in defence supply chains has emerged as a pivotal area of focus. Despite its importance, the existing literature on integrating commercial logistics suppliers into defence supply chains, especially for preparedness purposes, remains scarce. This research aims to address this gap by synthesising existing literature, highlighting key research gaps, and proposing a conceptual framework for governing buyer-supplier relationships across the security crisis spectrum – from peace to armed conflicts

Methodology

A systematic literature review was conducted to analyse academic papers published up to 2024 on collaborations between defence organisations and commercial suppliers within the defence logistics domain.

Findings

The literature largely overlooks the role suppliers play in transforming defence logistics from peacetime to crisis/war operations. The findings underscore the necessity of a dual-focused approach to cater to both peacetime and crisis/war requirements, recognising that the dynamics and success factors of defence-commercial collaborations vary across the security crisis spectrum. We suggest that more research is needed on collaborations during military operations, on the collective transition from peace to crisis or war operations, and the implications of public procurement legal framework on the ability to foster preparedness collaborations.

Research limitations/implications

The sample of papers is retrieved from Scopus and Web of Science using specific search-strings, potentially omitting relevant studies that do not match the search query.

Practical implications

This study highlights the need for defence organisations to proactively foster collaborative relationships with suppliers, adapt contractual and relational governance mechanisms, and address regulatory barriers to enhance logistics preparedness. It further provides insights into how defence organisations can effectively leverage suppliers’ capabilities and resources to ensure operational effectiveness across the security crisis spectrum.

Social implications

This research emphasises the importance of a cohesive approach to national defence that includes both defence and civilian sectors. Fostering collaboration between these entities can strengthen societal resilience and improve the collective response to emergencies.

Original/value

This study contributes to the existing body of knowledge by providing a comprehensive overview of the challenges and opportunities in collaborations between defence and commercial logistics suppliers. The proposed conceptual framework and research agenda offer a novel perspective on logistics preparedness, with significant practical and societal implications for enhancing defence capabilities and resilience in an increasingly unpredictable security environment.

Introduction

The last two years have seen a fundamental shift in the security of maritime supply chains, directly effecting the security of supplies for the German economy. Germany is the third largest economy in the world while at the same time almost entirely dependent on imports of natural resources to drive its production. Furthermore, its economic model focusses predominantly on exports. Both imports and exports in their vast majority are transported by sea.

From COVID-19 via the Russian invasion in Ukraine, to the effects of the attacks of the Houthi rebels on vessels in the Red Sea, Germany’s vulnerability with regard to its maritime supply chains has become strikingly obvious.

Methodology

This topic is approached by analyzing open-source literature, as well as shipping and trade data. The goals are to identify the impact of geopolitical events on maritime supply chains critical for Germany, i.e. identifying exemplary critical raw materials and products and how their supply is affected. Additionally, how are important ports affected by this new reality. To achieve this, the authors look at imports for Germany by sea – especially for the exemplary case of the port of Hamburg, data from shipping companies and freight rates to determine the impact of these threats. The aim of this study is to establish a framework of reference for comparisons and to monitor changes.

Findings

The research reveals that the global system of maritime transport fortunately has a degree of in-built resilience. For example, over-capacities in the shipping-market went a long way to cushion the blow to global supply chains, when the Houthi-attacks began in fall 2023. At the same time, a shift in trade-patterns with a clear geopolitical dimension is discernible – an indicator of more than just a temporary disruption caused by the attacks on shipping. Furthermore, the fact that chance seemed to have played a big part in softening the effects of these latest disruptions suggests that conscious efforts should be undertaken to analyze and monitor maritime risks more closely and increase resilience – reduce exposure and prepare for speedy recovery, regardless of the origin of ultimately inevitable disruptions, be they intentional, accidental or the result of natural disasters.

Discussion

The current realities show that the topic resilience of maritime supply chains has to become more of a focus for Germany. New concepts, models and frameworks for analyzing, monitoring and responding to a broadest possible range of potential disruptions need to be developed to make the German economy more resilient to threats.

Conclusion

The sea, maritime transport, maritime infrastructure, resources and the entire oceanic ecosystem are crucial to human development and the viability of modern societies. Accordingly, threats to maritime supply chains are of great importance. Especially with the proliferation to these threats driven by geopolitical rivalry, in combination with an increased likelihood of natural disasters as a result of climate change, promoting maritime resilience is a strategic imperative.

Given the persistent energy crisis and the increasing demand for green electricity, the significance of producing green hydrogen is continually rising. The state-of-the-art process for generating green hydrogen is electrolysis which requires large amounts of pre-treated freshwater as the feedstock. This poses challenges for global drinking water supply and demands significant amounts of energy for treatment.

In contrast, the SeaEly project aims to use seawater directly as the feedstock for the electrolysis. This approach leverages the abundance of seawater worldwide and has potential to save substantial amounts of energy that would otherwise be used for water pre-treatment. To achieve the project’s goals, special membranes are being developed to withstand the high salinity of seawater.

A byproduct of this electrolysis process is seawater brine. Current disposal strategies for similar seawater brines, such as those from desalination plants, often involve returning them to the sea, despite the known negative effects on the marine ecosystems.

As part of the SeaEly project, this work provides an in-depth analysis of sustainable and environmentally friendly applications for seawater brine, instead of returning it to the sea. It also includes economic feasibility estimations for mineral extraction and commercialization. Therefore, this approach emphasizes social and environmental responsibility within the supply chain.

In this study separation strategies for the fractionated extraction of various minerals are being developed, considering the abundance of different elements in the Weser River near Bremerhaven. The considered separation strategies include ion exchange, adsorption, liquid-liquid extraction, precipitation/crystallization, and membrane processes.

The economic feasibility of the separation is estimated considering market demands and raw material prices, extraction yields and occurring investment and operating costs. This study concludes that the extracting of chlorine (Cl), sodium (Na), magnesium (Mg), and calcium (Ca) from the resulting brine holds significant economic potential. Over a three-year period, the extraction of Cl, Na, Rb, Mg, and Ca from the brine can yield substantial net revenues ranging from 2 × 10³ , to 80 × 10³ based on the volume of untreated seawater, depending on the method employed and local market conditions.

A SWOT analysis reveals the project's unique strengths, including the production of green hydrogen from an abundant resource and the scalable extraction of raw materials. However, challenges such as complex extraction processes and limited technological adaptability pose internal weaknesses. External opportunities include supplying a variety of raw materials through environmentally favorable byproduct utilization within a circular economy and therefore counteracting existing supply chain risks. Yet, the project faces threats, such as the complex and variable composition of seawater at different locations and uncertainties regarding large-scale implementation necessitate careful consideration of the project's viability.

Many countries around the globe have taken a variety of strategic policies aiming at meeting energy needs more sustainably. Hydrogen energy demand has grown remarkably to support commercial, transport, and residential applications. In this study, we assess the upstream process of the Hydrogen Supply Network (HSC) based on the green hydrogen concept, in which electricity from a renewable energy source such as solar renewable energy is used in the electrolysis technique to break down water into hydrogen and oxygen. Initially, multiple criteria based on geographic, climate, and sun-earth interaction data are collected inclusive of locational area, population density, precipitation level, days with rainfall, air temperature, humidity percentage, wind force, sunshine hours, solar irradiation, and photovoltaic power. Then, these conflicting criteria are modeled using one of the well-known Multiple-Criteria Decision Analysis (MCDA) called the Data Envelopment Analysis (DEA) technique to evaluate the relative efficiency of each investigated regional location for a potential solar energy plant. The DEA technique, in particular, is a linear programming and production theory-based nonparametric approach generally used for efficiency analysis and optimization. The technique thus allows for the measurement of the relative efficiency of alternatives called Decision Making Units (DMUs) simultaneously to capture the interaction between multiple input and output criteria.

Next, given several efficient DMUs, the super-efficiency DEA technique (SDEA) is further used as a basis for ranking efficient DMUs under consideration. The SDEA score, in particular, can be used to measure the proportional increase in the inputs for a DMU that could take place without destroying the ‘efficient’ status of that DMU relative to the efficiency of the remaining DMUs. Thus, the SDEA score can further provide a measure of stability for obtained results. The case study of the state of Saxony-Anhalt in Germany inclusive of eleven districts and three independent cities is further used as DMUs to explore the district/city’s relative efficiency. Our initial DEA analysis suggests that about 57% of all DMUs (i.e., eight DMUs) perform relatively efficient, which are found to be Börde district (BK), Burgenland district (BLK), Harz district (HZ), Mansfeld-Südharz District (MSH), Saalekreis (SK), Salzlandkreis (SLK), City of Halle (Saale) (HAL), and State capital Magdeburg (MD) with all reported 1.00 efficient scores. This result suggests benchmarking locations for solar energy plants in the green HSC. Additionally, the SDEA technique is further used, which enables efficient DMUs to be ranked. That is, the efficiency score in SDEA allows the efficiency score for efficient units to be higher than 100% and thus the efficiency score from the SDEA technique can be taken as a basis for a complete ranking of efficient units. Accordingly, our analysis suggests that the top three efficient locations for solar energy plants in the green HSC based on the SDEA technique are HAL followed by HZ and MD, respectively. We note that this study is the first phase of our ongoing research framework to model and analyze HSC to integrate the upstream, midstream, and downstream operations.

In the discussion of the reduction of greenhouse gas emissions, the use of hydrogen as an energy carrier in the road transportation sector, particularly in freight transportation, is regarded as one option to reduce mobility-based emissions. In this context, the efficient supply of hydrogen refuelling stations is crucial element in the total hydrogen supply chain. Various options for supplying hydrogen refuelling stations from a source, in terms of transportation and storage technologies, already exist, and with advances in research new options continue to emerge. These options, also referred to as pathways, are associated with different costs and also emission characteristics, which strongly depend on the considered scenario. In this paper, a methodology is proposed that allows for flexible modelling and evaluation of hydrogen delivery pathways. The methodology is based on the concept of activity analysis. Delivery pathways are modelled as sequences of activities with all associated material flows and emissions. This allows for both economic and environmental assessment of different hydrogen delivery pathways. The applicability of this approach is demonstrated within a case study for two different use cases. First, we show how different delivery pathways for a specific sourcing option can be evaluated. Second we apply the proposed method for the integrated assessment of different sourcing options, locally procured grey hydrogen and imported green hydrogen. Enabling an economic and environmental evaluation, the methodology allows for the identification of efficient solutions.

Purpose: Container terminals face intense competition, with handling costs and processing times directly influencing their competitiveness. Ship-to-shore (STS) cranes play a pivotal role in container terminal operations, responsible for handling containers between quay and vessels. Therefore, the productivity of STS cranes is a critical performance metric. Idle time in STS crane operations results in productivity losses across the entire terminal system. Common port operating systems include STS cranes and terminal trucks (TT) working in tandem, with TT responsible for horizontal transport between the STS cranes and yard. The interaction between STS crane productivity and TT availability presents a significant operational challenge: insufficient TT leads to STS crane idle time and reduced productivity, whereas a surplus of TT results in TT idle time and inefficiencies. This research addresses this operational objective conflict through the application of logistics operating curves (LOC).

Methodology: The study begins by identifying relevant influencing, control, and target variables. The researchers then establish a discrete-event simulation (DES) using Tecnomatix Plant Simulation 2201 to generate output crucial for parametrizing the LOC. They propose a comprehensive framework that encompasses the relationships between inputs, outputs, the simulation model and the LOC. Additionally, they provide a schematic structure of the simulation, detailing the key functionalities implementing the handling processes in the model.

Findings: The expected results demonstrate that employing the DES model to accurately simulate the unloading process of a container ship using STS cranes and multiple TT is a viable method for constructing the LOC. The output data from the simulation allows the parametrization of the LOC for STS crane productivity relative to the number of TT. This operating curve highlights the trade-off and provides insights into optimizing the balance between STS crane and TT productivity.

Originality: This research contributes to literature on maritime logistics especially container terminal operations by addressing the operational conflict through the application of LOC. A method originally developed for production logistics, now adapted to maritime logistics. The proposed simulation model effectively captures the dynamics of STS crane operations. The framework and schematic structure detailed in this work provide a structured approach to developing and implementing the simulation model. The application of LOC can guide port operators in making data-driven decisions to enhance operational efficiency and competitiveness. Future research should refine these models and explore their applicability across diverse port environments to validate their effectiveness and generalizability.

The growing demand of global trade has evolved logistic operations at ports to adopt high-tech equipment’s with intelligent and automated moves. Although the application of automation technology has increased the productivity, but evidence has been found where in some cases automated machines had led to potential loss due to inefficient terminal design decisions ^[1].

The project’s goal is to investigate the operational aspects of single and double trolley ship to shore (STS) container cranes, and evaluate their performances based on a hypothetical automated terminal design concept. The discrete event simulation will be used to conduct experiments for different terminal design configurations and identify the performance of STS crane with parametric variations in operations. The impacts on the terminal key performance indicators (KPI) will also be identified and compared for both the crane types.

The project work involves both the qualitative and simulative studies. The qualitative study includes the desk research on available STS cranes in the market through manufacturer websites, publications, and reports. Recent container terminal news and survey reports will also be studied to identify a trend in the choice of STS crane by terminal operators. The simulative study will cover the aspects of simulation modelling and experimentation. In this, abstract simulation models for individual terminal components will be created. These models will include the state-of-the-art different means of automated horizontal transports and automated stacking cranes. The STS crane will be modelled with sub-models so that both the single trolley and double trolley cranes can be configured. The data for the simulation experimentation will be synthesised based on the expert reviews and raw data resources presented by publication partner (KALP GmbH).

Simulation experiments within present scope of study focuses on the evaluation of crane performance based on the position of lashing operations (water side and land side) for both single trolley and double trolley cranes. The study also involves the analysis of crane dynamics based on container position within vessel stowage plan during loading and unloading operation. Within the study both the coupled operations and decoupled operations will be studied individually, and in each case the transfer zone of container will be kept in the back-reach of the STS crane. Simulation experiments will be developed, and results will be documented in forms of graphs and performance charts representing efficiency and productivity.

The simulation results obtained from the hypothetical terminal design concepts will be explained and supporting statements will be drawn in the outlook of the study. Graphs and charts associated with crane performances will be elaborated and design recommendations will be drafted for attaining maximum productivity and optimum efficiency. Furthermore, general statements of recommendations for the reader will be drafted about the choice of decision over crane type selection and the parameters affecting such decision.

Source

[1]: N. Haworth, „Lessons in failure: Automation at the port of Auckland,“ ITFGLOBAL, 2023.

Transportation plays a key role in creating mobility for both freight and people thus facilitating growth of various industrial sectors. Tanzania is a gateway to in-transit cargo destined for landlocked neighbouring countries of Rwanda, Burundi, Democratic Republic of Congo and Zambia. Cognizant of this advantage, it envisages becoming an industrial hub of the East African region by 2025. Amidst this vision, Tanzania experiences an increasing vehicle density rate and the highest fatality rate (Kircher & Andersson, 2013) that is partly attributed to an increase of affluent middle class and rapid urbanization on the one hand, and the increase of in-transit cargo through the port of Dar es Salaam on the other.

At the centre of dense traffic, truck drivers are an important segment taking charge of the transportation operations. They facilitate right time, quantity and location delivery (Koberg & Longoni, 2019; Speranza, 2018). However, Tanzania’s transportation sector is vulnerable to increased inefficiency as a result of overly restrictive regulation in multiple ways. For example, there are heightened vehicle weight controls and several speed bumps installed on road highways. We argue that such overly regulation makes it uncompetitive relative to comparator countries. Such measures demand high compliance levels by truck drivers who struggle to meet their tight turn–around–time (TAT) schedules imposed by cargo owners and their employers. Existing literature documents more on drivers as dominant players of the sector by focusing on their emotional exhaustion (Nakata et al., 2022), long working hours (Hege et al., 2019) and loss of life and freight due to poor infrastructure (Girotto, et al., 2019) and corruption (Garbarino et al., 2018) as risk factors that jeopardize their overall performance (Semeijn et al., 2019). Undoubtedly, competitiveness of the sector depends on a number of factors working together as a system for which the current literature falls short. To fill this gap, we address the following research question: what is the cumulative systemic effect of the various dimensions related to the sector? In response to this question, we present a holistic framework of the combined effect of truck drivers, vehicles, infrastructure and legal enforcement with a view to broadening our understanding of the matter.

Theoretically, due to inclusion of multiple perspectives affecting transportation sector efficiency, this study benefited from the use of an ecological model of health behaviour and the Principal-agent theory. We administered a questionnaire to 112 respondents from six Logistics companies, one Association of truck operators in Tanzania and the Traffic Police department. Some in-depth interviews were also held with a few informants from the Ministry of Transport. The data were analyzed through factor and multiple linear regression analyses.

Findings indicate that transportation inefficiencies can be addressed by enhancing the combined effect of drivers, infrastructure status, legal administration and vehicles’ road worthiness as opposed to individual factors even though driver-related factors are the most significant. These findings point towards harmonization of policies across ministries in order to make the sector competitive and resilient against the surge of transit cargo thus facilitating the industrialization process.

The increase in global logistics and supply chain and the integration of logistics information communication technology (LICT) have created a new business model for organisations as they compete to balance supply and demand to satisfy consumers. As the new business model evolves, it poses more threats and challenges within the business environment. These changes have spurred proliferation of sophisticated LICT for leveraging agility and visibility in supply, especially product distribution to enhance productivity and, most significantly, anytime, anywhere, anyhow, through any device delivery and receiving of products to the customers. The degree to which SMEs distribution strategies have been innovatively reconstructed through LICT to improve SMEs distribution relationship network and performance within the South African context has not been scientifically investigated. This study investigates how the adoption of LICTs helped SMEs reimagine their distribution chains to enhance network relationship. Hence, the proposed study timely and relevant. By this means, scientific recommendations will emerge based on the SMEs distribution relationship network for operational improvement and resilience, which could contribute significantly to the economy's GDP. The main objective of this research is to structurally model the impact of LICT on SMEs distribution relationship network and distribution service performance.

The study employs a quantitative survey method and a purpose-sampling technique to collect comprehensive data from SMEs in different sectors within the Northern Cape, South Africa. Data collected analysis techniques to be used include factor analyses and structural equation modelling. Analysis would be carried out using the statistical package for the social sciences (SPSS) software, SMART-PLS version (4.0) and the R programming language. The findings of this study are expected to help SMEs practitioners and policy makers understand the benefit of the distribution relationship network through LICT compatibility and how investment in these areas can be prioritised to improve SMEs and economic growth. Furthermore, the finding would assist SMEs competition regulatory bodies in mediating or moderating the impact of technology-enabled distribution relationship network on distribution performance. Future research directions are also expected to emerge from this exploratory study.

Note: This research is funded by Sol Plaatje University's MIT Research Grant.

Purpose: Electric cargo cycles (ECCs) can be seen as a good alternative to conventional delivery vans for last-mile delivery. With their wider implementation, battery range is frequently mentioned as one of the main limitations of ECCs. Fast charging and battery swapping are two potential technologies to address this challenge. This paper preliminarily explores the feasibility of adopting battery swapping and fast charging to address the battery range limitation of ECCs, and provides insights on future research directions.

Methodology: the study is based on a systematic literature review. Afterwards, a content analysis is performed. Additionally, some grey literature regarding the latest technical parameters of charging technology was analyzed as well, in order to consider state-of-the-art technologies. Collected data is cross-checked in a prudential way from the official website of more than one manufacturer.

Findings: due to their wider adoption, ECCs are likely to work longer hours and more frequent shifts, often traveling over 100 km and working more than 10 hours per day. During daily operations, the idle time is usually very short. Riders sometimes find it difficult even to take lunch breaks due to the high work intensity. Moreover, the duration of reloading parcels when going back to depots and the idle time in which messengers stop and wait for the parcel to be signed for receiving is around 5 minutes, which is not long enough for battery recharge.

Regarding battery range, manufacturer estimations often fail to match real-world energy consumption in urban last-mile delivery. Rider assistance has a limited impact on extending electric range. Additionally, environmental factors such as weather and terrain can negatively impact battery range and increase rider fatigue, reducing pedalling assistance. To have a more comprehensive understanding of the battery range of ECCs, 20 factors affecting battery range and their interrelationships are identified and a conceptual framework is developed. After that, battery swapping and fast charging options are analyzed and compared based on charging speed, infrastructure requirements, and technical challenges.

The evidence from the literature is that when a single battery capacity can't meet distribution requirements, the fastest charging technology available may not suffice for enough rapid energy replenishment. Equipping ECCs with smaller, easily swappable batteries is then a promising solution for robust delivery operations without long breaks, despite higher initial investment.

Originality: This paper is one of the first papers that discusses the potential of employing battery swapping and fast charging technologies on ECCs for last-mile delivery, providing new insights into these emerging concepts. Furthermore, this paper develops an original framework that identifies and analyzes the factors affecting the battery range of ECCs, providing a comprehensive understanding of the vehicle performance characteristics in real-life operational conditions. Additionally, research gaps are identified, and further potential directions are proposed.

Purpose: For efficient and effective promotion of sustainability in urban transportation, the predictors of transportation like ridesharing is imperative. The study aimed at segmenting urban dwellers basing on their perceptions in the use of ride-sharing. Specifically, the perceived benefits of ride-sharing including cost, social, sustainability, and efficiency were related to satisfaction with ride-sharing. The Social Exchange Theory integrating sustainability were the theoretical lenses for the study.

Methodology: A self-administered questionnaire was used for data collection. A proportionate and convenient sampling strategies were used to pick respondents in the three districts of Dar es Salaam, a commercial and largest city in Tanzania. A total of 613 valid cases out of 630 were used for analyses. Using the Chi-Square Automatic Interaction Decision (CHAID) tree, the respondent’s satisfaction with ride-sharing was used as the dependent variable while the benefits (efficiency, costs, social, and sustainability) were used as predictor and grouping variables.

Findings: the decision tree yielded 5 different segments with efficiency, cost, and sustainability in that order to be relevant predictors for satisfaction. The highly satisfied group tended to focus more on efficiency as well as being cost conscious. The 5 segments were further explored using one-way Analysis of Variance (ANOVA) with demographic variables. Among the demographics, age, income, occupation, and place of residence can be used to further differentiate the segments.

Originality: The paper extends the use of CHAID into the context of urban logistics and sustainability in a less researched sub-Saharan African context. Practically, the results provide insights on how to market ride-sharing both online and offline for higher impacts including sustainability aspects. Specifically, to marketers of ridesharing can prioritize efficiency and costs in their marketing strategies. As sustainability was the last to be included in the segmentation, sustainability advocates need to undertake more social marketing to change the attitudes favoring sustainability. Theoretically the results offer support to the Social Exchange Theory with the three significant factors impacting satisfaction with ridesharing in urban context of sub-Saharan African country.

Globally, organizations are developing strategies to address climate change, with decarbonization and net-zero commitments emerging as essential steps for sustainable development. Achieving a net-zero future relies on increasing research, development, and testing of existing technologies.

Maritime ports play a critical role in logistics networks. Given the complexities of port decarbonization, a combination of measures is necessary, as no single solution fits all scenarios. However, several barriers hinder low-carbon operations, making it challenging for ports to plan and implement decarbonization strategies. Existing studies usually focus on the barriers of specific measures, which is insufficient for port decarbonization. Also, previous research has identified a need to explore further port decarbonization barriers (Fadiga et al., 2024).

This review aims to identify the barriers hindering maritime port decarbonization and provide a systematic categorization of the identified barriers to help researchers and industry stakeholders prioritize interventions and customize strategies. We have selected 33 publications through the Systematic Literature Review (SLR) methodology to investigate the key barriers hindering maritime port decarbonization. By doing so, 25 barriers were identified and categorized, offering detailed definitions for each barrier. Additionally, the study describes mitigation strategies to overcome port decarbonization barriers. These suggestions provide actionable solutions that stakeholders may implement to facilitate the transition to sustainable port operations. They also assist policymakers and port authorities in effectively overcoming these obstacles and advancing decarbonization efforts.

A significant gap was revealed as most articles do not comprehensively categorize the barriers and those that do lack the application of any theoretical lens in their categorization. Regarding the theoretical lenses used, most articles do not specify a theoretical framework, with the Diffusion of Innovation (DOI) model and the Technology-Organization-Environment (TOE) framework being the most referenced.

We suggest that future studies employ other theoretical lenses, such as the Sociotechnical Systems (STS), to categorize the identified barriers and provide valuable insights for researchers and practitioners seeking to develop strategies to overcome these barriers and promote sustainable maritime operations. By categorizing these barriers through the STS theoretical lens, it is possible to explore the complex relationships between socio-technical components and various actors in the maritime transportation sector, emphasizing the need for integrated approaches for effective decarbonization strategies.

The study further highlights the barriers' interrelationships, emphasizing the importance of a multifaceted approach to addressing the complexities of port decarbonization. Future research should focus on the interdependence of challenges to decarbonizing maritime ports to ensure sustainable operations in the maritime transport sector.

References

Fadiga, A., Ferreira, L. M. D., & Bigotte, J. F. (2024), “Decarbonising Maritime Ports: A Systematic review of the literature and insights for new research opportunities”, Journal of Cleaner Production, 142209, https://doi.org/10.1016/j.jclepro.2024.142209

Container Terminals are often subject to high load peaks, which can be caused by fluctuations in truck arrivals, delays in train and ship arrivals during the day. As a result, these peak times lead to longer waiting times for trucks within the terminal. At the same time, this has a negative impact on operational efficiency of the terminal, as it can cause inconsistent utilization of the handling equipment at various times of the day. In order to improve the organization of their processes, most of the seaport container terminals are introducing truck slot booking systems that can clearly define the time slots in which trucking companies can deliver and collect containers. These truck slot booking systems primarily have the effect of smoothing the peak load from the terminal's perspective throughout the day. However, for the trucking companies, they increase the complexity of transport planning and thus shift inefficiencies between the parties involved.

Aim of the FLEXIKING project is to develop a collaborative and flexible truck slot booking system, which continuously recalculates the available time slots at the combined transport terminal, considering the current ETAs of inbound and outbound traffic. At the same time, the system balances the interests and degrees of freedom of the trucking companies and the terminals by enabling a dynamic adjustment in the event of changing framework conditions, achieving consensual rescheduling of time slots.

Within the project, five innovation areas were defined to achieve this goal. Aim of this publication is to predict the waiting time of trucks in each time slot to find the intended truck quota for each of the slots, considering the operational parameters of the terminal. This should help trucking companies plan their routes and terminals to optimize the use of their equipment as well. Therefore, existing queuing models will be adapted for calculating the waiting times within the time slots. As the first step, the processes of a combined transport terminal were mapped at a particular terminal location of a project partner and analysed in detail. Moreover, the length of time slots was determined. In the second step, the queuing model was developed. In addition to arrival rates and waiting time probabilities, the queuing model also considers other parameters such as the share of direct transshipments, predicted rail delays, operational efficiency, or the inventory levels. A transferability check ensures that the assumptions made in the model development are transferable to other terminals.