Logistics

Background: Road transport companies utilize transport capacities as fixed compositions of tractors and semi-trailers, while the possibility of exchanging semi-trailers is considered ad hoc, after some unforeseen circumstances emerge on the route. Such an approach is a limiting factor in achieving optimal utilization of transport capacities, and consequently affects profitability. We proposed a new concept of vehicle fleet management where semi-trailers would not be permanently assigned to the tractors, but could be dynamically reassigned, to utilize optimally the vehicle fleet, on considering the planned itinerary, the driver’s working hours, and traffic conditions. Methods: We set the key performance indicators and developed a prototype application based on the concept of dynamic semi-trailer allocation. We simulated the use of the prototype application on the historical data collected in the case study on one of the leading transport companies in the cold chain, and evaluated the benefits that may be achieved thereof. Results: Simulation showed that implementing the proposed concept enabled the reduction of the vehicle workdays spent on-the-route, the reduction of the number of vehicle compositions needed to handle the transport demand, and improved the quality of service. Conclusions: The proposed concept is beneficial for transport companies that operate large vehicle fleets on long-haul routes, with several transport orders per route. The prototype application may be the basis for developing a fully functional application that can be integrated into the fleet management system. Full article

The Integral Ergonomic-Value Stream Mapping (Ergo-VSM) methodology is introduced in this study, which is tailored for the metal-mechanic sector and aims to assess the operational and ergonomic conditions of production processes. The methodology is designed to identify ergonomic risks and propose an improvement plan to increase productivity by integrating ergonomic measurement instruments aligned with official standards and lean manufacturing tools such as VSM and Kaizen. The study, which was conducted in a metal-mechanical MSME (micro, small and medium enterprises), resulted in an 11.8% overall improvement in psychosocial risk factors, a 4.4% increase in productivity with a 1.96-h reduction in cycle time, and a 20% decrease in reported quality rejections. Notably, the study shows that the Integral Ergo-VSM can be implemented in a variety of organizational contexts, ensuring adaptability without jeopardizing the methodology’s core objectives. Full article

Background: Industry 4.0 signifies a profound global transformation in production and service activities through its novel organizational principles, including digital connectivity, information transparency, technical assistance, and decentralized decision making. This transformation poses significant challenges for businesses, particularly small- and medium-sized enterprises (SMEs). In response, maturity models have been developed and adapted to facilitate a transparent and supportive entry into this transformative domain. Method/Aim: This study is dedicated to the comprehensive analysis of Industry 4.0 maturity models through a systematic literature review to identify and evaluate previously published recommendations for the adoption and utilization of Industry 4.0. The aim is to provide valuable insights in this context, with a particular focus on easing entry into this domain for SMEs. Results: Quantitative findings reveal a growing demand for fundamental support when entering this domain, with maturity models capable of meeting the demand for structured guidance. However, these models are currently under-validated, lacking transparency, and are often unsuitable for SMEs. Qualitative results categorize numerous insights and recommendations into ten distinct categories related to Industry 4.0. Conclusions: This paper provides a structured summary to support newcomers, research institutions, and businesses in effectively initiating and optimizing their Industrsy 4.0 activities. Full article

Background: Last-mile delivery (LMD) is a challenging and costly supply chain process that involves direct customer interaction. In a recent study by the World Economic Forum, it was emphasized that last-mile logistics is an emerging research area. In particular, the European Commission Directorate General for Energy and Transport provides guidelines for abnormal road transport, such as heavy, bulky, and oversized (HBO) products. In the last few years, many research articles have been published on the topic of LMD. Several literature review studies from the perspective of different stakeholders have also been published. The delivery of HBO products is more challenging in many ways compared to parcel delivery. Although HBO products yield the most profitable sales, the LMD of HBOs is less studied compared to parcel deliveries. Methods: This research employed a descriptive review method to scan 195 published articles and reports related to LMD and HBOs. Results: Our results identified the challenges in the field and identified the need for separate approaches for different customer segments, such as rural and urban customers, as well as older and younger customers, when formulating delivery strategies. Conclusions: This paper highlights the research gap in this field by using a descriptive literature review methodology and presents academic contributions available so far, as well as challenges for further research. Full article

Background: This paper explores the potential of Industry 5.0 in driving societal transition to a circular economy. We focus on the strategic role of reverse logistics in this context, underlining its significance in optimizing resource use, reducing waste, and enhancing sustainable production and consumption patterns. Adopting sustainable industrial practices is critical to addressing global environmental challenges. Industry 5.0 offers opportunities for achieving these goals, particularly through the enhancement of reverse logistics processes. Methods: We propose an integrated methodology that combines binary logistic regression and decision trees to predict and optimize reverse logistics flows and networks within the Industry 5.0 framework. Results: The methodology demonstrates effective quantitative modeling of influential predictors in reverse logistics and provides a structured framework for understanding their interrelations. It yields actionable insights that enhance decision-making processes in supply chain management. Conclusions: The methodology supports the integration of advanced technologies and human-centered approaches into industrial reverse logistics, thereby improving resource sustainability, systemic innovation, and contributing to the broader goals of a circular economy. Future research should explore the scalability of this methodology across different industrial sectors and its integration with other Industry 5.0 technologies. Continuous refinement and adaptation of the methodology will be necessary to keep pace with the evolving landscape of industrial sustainability. Full article

Background: The primary objective of this study was to examine the advancements in sustainable supplier selection through multi-criteria decision making (MCDM) from the years 2013 to 2022. In the recent past, researchers have carried out a significant amount of research in this field over the course of several years; Methods: a total of 121 scientific publications sourced from the Scopus database were chosen for analysis, employing the bibliometric method and graphical visualization of the VOS viewer application to visually analyze and map research networks and collaboration patterns, aiding in the evaluation of scientific impact and knowledge dissemination; Results: the findings of this study indicate that the research trend in sustainable supplier selection through MCDM witnessed its most significant growth in the year 2019. Researchers predominantly disseminated their scientific findings through articles, accounting for 81% of the publications, followed by conference papers at 14%, and book chapters at 2.5%; Conclusions: the primary area of focus in these studies pertains to decision-making processes involved in sustainable supplier selection. The implications and theoretical contributions derived from this research, coupled with the latest advancements, serve as a foundation for further exploration and development of sustainable supplier selection research through MCDM. Full article

Background: Endogenous and exogenous factors impact the operational characteristics of supply chains, affecting wholesale warehouses. The survival of a warehouse is often threatened by disruptive events that alter infrastructure and performance. The emergence of COVID-19 exemplified the need for adaptability in retail goods supply chains, emphasizing the necessity for responding to external shocks. Methods: The MARLIN (Method wArehouse ResiLience dIstruptioN) method, founded on theories and models of resilience engineering is introduced. MARLIN is a practical tool designed to identify key areas requiring intervention in response to disruptive events. An empirical test was conducted in an Italian warehouse. Results: The conducted test yielded tangible results, demonstrating the efficacy of the method. It successfully pinpointed areas necessitating intervention and identified Key Performance Indicators (KPIs) associated with disruptions. The study not only underscores the importance of data collection but also highlights the often-overlooked significance of warehouse management. Conclusions: The study establishes MARLIN as a valuable asset for stakeholders involved in disruption management. Its application has proven instrumental in recognizing areas of intervention and identifying KPIs related to disruptions. Ongoing research endeavors to broaden its applicability across diverse supply chain scenarios, aiming to enhance situational awareness and enable proactive risk assessment through what-if analysis. Full article

Background: The logistics industry is keen to meet customer expectations, which in turn supports trade by enhancing business competitiveness and promoting economic development. Logistics companies realized that fulfillment of customer needs has a significant effect on their business survival, their sales and ultimately their profits. This research analyzes the variables that influence customer satisfaction with parcel delivery services in the Kingdom of Saudi Arabia (KSA), particularly the SERVQUAL dimensions: tangibility, reliability, responsiveness, assurance, and empathy. The research also examined whether national culture affects customer perceptions of parcel delivery services in KSA or not. Methods: The research collected data using a self-administered survey, which was directed at a sample of customers in the KSA, who had recent experience with parcel delivery services. Results: The results of statistical analysis showed that the best predicators of customer satisfaction are reliability, followed by the dimensions of empathy, then tangibility and finally responsiveness. In addition, the results showed significant differences in customers’ overall satisfaction between Saudis and non-Saudis. Saudis were more satisfied with the service than non-Saudis were. Conclusions: This research showed the key role of national culture in perceiving parcel delivery service quality in KSA. The findings provide important messages to the administrators of the logistics services in KSA, which have become crucial for the Saudi Vision 2030. They need to customize their service according to customer needs and provide regular training for their staff to be supportive, reliable and have the credibility to deliver services as promised. Full article

Background: Scholars have indicated differences in the attitudes of urban and non-urban populations, especially after the COVID-19 outbreak, which extend to their needs and expectations regarding rail transport development. The aim of this study is to enhance the quality of train services in the post-pandemic era, and multigroup analysis will be applied to achieve the difference in area context. Methods: The research data were collected from rail transport users throughout Thailand, consisting of 665 urban and 935 rural users. The questionnaires primarily focused on user expectations regarding rail service quality and travel conditions in the post-pandemic landscape using multigroup confirmatory factor analysis (MCFA). Results: The results unveiled significant variations in user trends and needs across different contexts and areas. In urban settings, there was a notably higher overall service expectation compared to rural areas. Specifically, urban users prioritized factors such as accessibility and service empathy, whereas rural rail users placed greater emphasis on staff quality and reasonable pricing. Conclusions: These findings furnish rail transport service agencies with valuable insights and guidance for comprehending their users’ needs. They can develop appropriate organizational strategies, service quality enhancements, and policy adjustments tailored to the unique demands of urban and rural areas in the post-pandemic era, thereby ensuring sustainability. Additionally, the methodology of multigroup analysis served as a significant scientific contribution; this showed that the statistical analysis of different area contexts in the study should not be ignored. Full article

Backgrounds: The transportation system within any mining project, which is responsible for delivering extracted ore to the crushing units or wastes to the wasting dumps as the destinations, poses a significant challenge in mining processes. On one hand, there are various transportation systems, notably the Truck–Shovel, the traditional method, and relatively newer and less common In-Pit Crushing and Conveying (IPCC) systems. On the other hand, choosing the most suitable system for a specific mining project depends on various factors, with technical aspects being one of the most critical. While there is extensive research on the Truck–Shovel system from a technical perspective, there is relatively limited research on IPCC systems. Methods: This research aims to carry out a comparative analysis of different transportation systems, encompassing Truck–Shovel, Fixed In-Pit Crushing and Conveying (FIPCC), Semi-Fixed In-Pit Crushing and Conveying (SFIPCC), Semi-Mobile In-Pit Crushing and Conveying (SMIPCC), and Fully Mobile In-Pit Crushing and Conveying (FMIPCC) systems. To achieve this goal, a technical index is introduced, which is based on three elements: the availability and the utilization of the system, as well as the consumption of power. This index will be developed as a system dynamics model, enabling the observation of each system’s performance throughout the operational lifespan of the mine. Results: Ultimately, based on the proposed method, the most effective transportation system based on the defined technical index can be identified at any time of the project. In this research, the Truck–Shovel system generally selected as the most preferred transportation system, except for two different periods. Conclusions: This study could successfully perform the selection among different transportation systems. Nevertheless, it was modeled and performed in a deterministic environment, but still the stochastic nature of the processes can be another topic of research. Full article

Background: Material Requirements Planning (MRP) is critical in Supply Chain Management (SCM), facilitating effective inventory management and meeting production demands in the manufacturing sector. Despite the potential benefits of automating the MRP tasks to meet the demand for expedited and efficient management, the field appears to be lagging behind in harnessing the advancements offered by Artificial Intelligence (AI) and sophisticated programming languages. Consequently, this study aims to address this gap by exploring the applications of Python in simplifying the MRP processes. Methods: This article offers a twofold approach: firstly, it conducts research to uncover the potential applications of the Python code in streamlining the MRP operations, and the practical examples serve as evidence of Python’s efficacy in simplifying the MRP tasks; secondly, this article introduces a conceptual framework that showcases the Python ecosystem, highlighting libraries and structures that enable efficient data manipulation, analysis, and optimization techniques. Results: This study presents a versatile framework that integrates a variety of Python tools, including but not limited to Pandas, Matplotlib, and Plotly, to streamline and actualize an 8-step MRP process. Additionally, it offers preliminary insights into the integration of the Python-based MRP solution (MRP.py) with Enterprise Resource Planning (ERP) systems. Conclusions: While the article focuses on demonstrating the practicality of Python in MRP, future endeavors will entail empirically integrating MRP.py with the ERP systems in small- and medium-sized companies. This integration will establish real-time data synchronization between the Python and ERP systems, leading to accurate MRP calculations and enhanced decision-making processes. Full article

Background: How can flexible applications of the space and infrastructure of urban distribution centers (UDCs) be organized to help lower demands on space and infrastructure in cities? The application of flexible use of space and infrastructure can improve the efficiency of a UDC, but the challenge lies in the organization of the application of flexibility. Methods: The goal of this research was to identify how flexibility can be organized to impact overall societal benefits for the stakeholders in UDCs. This explorative and qualitative research was applied to the case of Food Center Amsterdam. Results: The results show that stakeholders have a limited understanding of the potential that flexibility can offer; that there is a need for an independent organizing capability and responsibility for collaboration on flexibility; and that a clear way to divide costs, benefits, risks, and opportunities in relation to stakeholder interests is required. Conclusions: Overall, flexibility shows potential to improve the efficient use of infrastructure and space. Further research avenues include the initiation of an organizing capability and distribution method for costs, benefits, risks, and opportunities between stakeholders. The remaining question is, can we get this organized in order to do more with less? Full article

Background: Systematic literature review (SLR) is increasingly utilized to maximize the element of rigor and minimize the individual bias of research synthesis. An analysis of the Web of Science (WoS) database indicates that 90% of the literature review studies using SLR have been published between 2012 and 2022. However, this progressive agenda is impaired by the lack of methodological consistency and rigorousness. To fill this gap, this paper aims at mapping the theoretical comprehension and practices of SLR and providing a stepwise approach to employing such a framework. Methods: A comprehensive narrative review is used in this paper to analyze the studies concerning the literature review typology and the structural assessment of the SLR. Furthermore, the methodological approach of the literature review studies that adopted the SLR and were published in the Logistics journal is assessed across a set of vital criteria associated with conducting an SLR. Results: There is a concrete link between the purpose of a review, i.e., to describe, test, extend, or critique, and the literature review type. There are 17 distinct literature review types, e.g., a narrative review, a bibliometric analysis, etc., which must be justified meticulously regardless of the SLR. The ambiguity in conceiving the SLR either as a toolkit or a review type, the lack of justification regarding the review purpose and type, and vague conceptual distinguishment between the bibliometric analysis, as a distinct review type, and the SLR framework, are only a few of the shortcomings observed in the analyzed papers. Conclusions: Given the significant role of SLR in elevating the element of rigor within the literature review studies, it is deemed essential to employ this framework by paying attention to two holistic factors: (1) theoretical distinction between the literature review purpose, the literature review type, and the SLR; (2) strict adherence to the SLR procedure with a high degree of accuracy and explicitness. Full article

Background: Logistics management in the clinical trials industry is a very challenging undertaking because it involves multiple stakeholders, complex processes, diverse software applications, intensive white-collar jobs, and onerous quality standards. Current business practices are inefficient and difficult to automate technologies. Methods: This paper reviews the theories and concepts of clinical trials logistics management. The inefficiencies in current logistics management industry are then addressed by building a conceptual framework based on contemporary software tools and architectures, such as web portals, software agents, business process management system, project cards, and resource cards, all interacting with specialized software applications such as accounting, inventory, and label design software. The framework supports data analysis at multiple levels of decision making. To this end, a project planning tool for facilitating and optimizing the operational planning in this industry is designed and presented. Results: The planning tool also contributes to the literature by contrasting several different resource scenarios such as the shared pool, dedicated resources for each project, and the creation of several work groups with dedicated resources. These are Pareto trade-offs. Conclusions: A framework employing a business process management is proposed for clinical trials logistics management. Different managerial scenarios with shared, dedicated, and work group resources are investigated using a case study. Full article

Background: In container terminals, optimizing the scheduling of external trucks and yard cranes is crucial as it directly impacts the truck turnaround time, which is one of the most critical performance measures. Furthermore, proper scheduling of external trucks contributes to reducing ${\mathrm{CO}}_2$ emissions. Methods: This paper proposes a new approach based on a mixed integer programming model to schedule external trucks and yard cranes with the objective of minimizing ${\mathrm{CO}}_2$ emissions and reducing truck turnaround time, the gap between trucking companies’ preferred arrival time and appointed time, and the energy consumption of yard cranes. The proposed approach combines data analysis and operations research techniques. Specifically, it employs a K-means clustering algorithm to reduce the number of necessary truck trips for container handling. Additionally, a two-stage mathematical model is applied. The first stage employs a bi-objective mathematical model to plan the arrival of external trucks at the terminal gates. The second stage involves a mathematical model that schedules yard cranes’ movements between different yard blocks. Results: The results show that implementing this methodology in a hypothetical case study may lead to a substantial daily reduction of approximately 31% in ${\mathrm{CO}}_2$ emissions. Additionally, the results provide valuable insights into the trade-off between satisfying the trucking companies’ preferred arrival time and the total turnaround time. Conclusions: The integration of data clustering with mathematical modeling demonstrates a notable reduction in emissions, underscoring the viability of this strategy in promoting sustainability in port-related activities. Full article

Background: Transportation demand forecasting is an essential activity for logistics operators and carriers. It leverages business operation decisions, infrastructure, management, and resource planning activities. Since 2015, there has been an increase in the use of deep learning models in this domain. However, there is a gap in works comparing traditional statistics and deep learning models for transportation demand forecasts. This work aimed to perform a case study of aggregated transportation demand forecasts in 54 distribution centers of a Brazilian carrier. Methods: A computational simulation and case study methods were applied, exploring the characteristics of the datasets through autoregressive integrated moving average (ARIMA) and its variations, in addition to a deep neural network, long short-term memory, known as LSTM. Eight scenarios were explored while considering different data preprocessing methods and evaluating how outliers, training and testing dataset splits during cross-validation, and the relevant hyperparameters of each model can affect the demand forecast. Results: The long short-term memory networks were observed to outperform the statistical methods in ninety-four percent of the dispatching units over the evaluated scenarios, while the autoregressive integrated moving average modeled the remaining five percent. Conclusions: This work found that forecasting transportation demands can address practical issues in supply chains, specially resource planning management. Full article

Background: A hospital’s operating theater service system is a large-scale, complicated system that must be carefully managed to offer the best possible results for its patients. Unlike other industries such as manufacturing and logistics, system dynamics (SD) methodologies are not extensively applied in hospital operating theaters. This study deals with the future development and possible future scenarios for hospital operating rooms in Bangladesh. Methods: Due to demographic dynamics and demographic processes, increased pressures on hospital care are expected in Bangladesh. The SD model anticipates possible future scenarios, reconciles service capacities and the variability of patient demand, and reduces patient congestion and waiting times in the hospital area. This study introduces a causal loop diagram to show a causal link between the hospital operating theater system variables. It also introduces a stock flow diagram to understand the dynamic behavior of the system. Results: The model validation testing reports that in extreme conditions, such as a 50% reduction in the patient arrival rate, the model is valid and runs as usual. Conclusions: This first work of SD modeling for hospital operating theater systems can help healthcare managers, decision makers, or researchers of any responsibility level make better predictions in order to reduce patient waiting times and backlogs and make appropriate decisions. Full article

Background: The air cargo logistics industry has been deemed reluctant to adopt new technologies for their business operations. So, this study aims to examine the adoption of technological innovations in the air cargo logistics industry in South Africa. The specific objective is to emphasise the effects of technologies on air cargo logistics operations to address the reluctance to adopt technological innovations in the industry. Methods: A questionnaire survey was conducted randomly on three hundred and seventy-three (373) cargo agents at the OR Tambo International Airport. The data were subjected to a nonparametric test with Kruskal–Wallis, exploratory factor analysis and regression analysis to explore the effects of technologies for prompt adoption and implementation of emerging innovations that enhance quality service delivery. Results: The study found that promptly adopting emerging technological innovations in the air cargo logistics industry promotes efficient operations, improves warehousing, and enhances cargo delivery services for customer satisfaction. Also, the study established that technologically driven operations and warehousing are significant determinants of quality service delivery in the air cargo logistics industry. Conclusions: This study encourages the prompt adoption and implementation of technological innovations for improved quality service delivery, customer satisfaction, and loyalty in the air cargo logistics industry. Full article