Maintenance dredging in ports and waterways is essential to ensure safe navigation. With increasing regulatory pressure on the maritime sector to reduce exhaust emissions, both dredging contractors and port authorities are seeking effective mitigation strategies. However, accurate emission estimates for maintenance dredging activities are still limited in the literature and often rely on experiential knowledge rather than scientific methodologies. This study suggests a method for estimating emissions and comparing alternative maintenance dredging strategies by quantifying trade-offs between project duration, energy consumption, and emissions. The method integrates vessel characteristics, project specifications, and sediment properties to allow for situation-specific, realistic assessments. A discrete-event simulation is used to evaluate two alternative scenarios, offering insights into the impact of key parameters on vessel selection and overall operational efficiency. The method is demonstrated using a case study of the Port of Ramsgate (UK), where estimated results are compared with real-world data for validation. Finally, the study outlines theoretical and managerial implications and suggests directions for future research.

Purpose – This study aims to identify and analyse the barriers to adopting Industry 4.0 technologies in inventory systems within the retail sector. Despite the critical role of these barriers in hindering the implementation of digital technologies, there is a noticeable gap in the literature regarding analytical studies that address this issue.

Design/methodology/approach – To fill this gap, the study employs a hierarchical model to examine the interrelationship between various barriers. The model integrates joint interpretive structural modelling (ISM) and cross-impact matrix multiplication applied to classification (MICMAC) analysis. The research involves interviews with a group of expert participants from the Australian retail industry, focusing on 13 key barriers identified through a comprehensive literature review and expert input. The driving power and dependence power of each barrier are assessed and classified into four clusters.

Findings – The study identifies 13 key barriers to the adoption of Industry 4.0 technologies in retail inventory systems. Among these, four stand out as the most influential: financial constraints, lack of management, organisational inadaptability and government reluctance. Financial constraints emerge as the dominant driver, as limited profit margins restrict retailers’ ability to invest in new technologies. In contrast, skill and training requirements were found to be the least consequential, indicating that workforce limitations, while relevant, are not perceived as critical in undermining inventory system performance. These results clarify the relative influence of barriers and their role in shaping adoption outcomes.

Practical implications – The study provides exploratory insights that can help retail practitioners in Australia understand and prioritise the barriers to adopting Industry 4.0 technologies in inventory systems. By mapping the driving and dependence power of each barrier, retailers can develop more targeted strategies to address the most influential challenges. While the findings are indicative and context-specific, they offer a structured basis for reflection and strategic planning, supporting the ongoing digital transformation of inventory management in the retail sector.

Originality/value – The contribution of this research lies in its context-specific examination of barriers to Industry 4.0 adoption in Australian retail inventory systems. Although previous studies have investigated Industry 4.0 adoption across various sectors, few focus on retail inventory management and the interrelationships among barriers in this specific context. By applying interpretive structural modelling (ISM) and MICMAC analysis, the study provides a structured exploration of how barriers interact, offering preliminary insights for both researchers and practitioners rather than claiming a fully novel methodological or theoretical contribution.

In the current era emphasizing sustainability and circularity, supply chain network design is a critical challenge for making reliable decisions. The optimization of facility location-allocation inventory problems (FLAIPs) holds the key to achieving dependable product delivery with reduced costs and carbon emissions. Despite the importance of these challenges, a substantial research gap exists regarding economic, reliability, and sustainability criteria for FLAIPs. This paper aims to fill this gap by introducing a multi-objective mixed-integer linear programming model, focusing on configuring a reliable sustainable supply chain network. The model addresses three key objectives: minimizing costs, minimizing emissions, and maximizing reliability. A notable contribution of this research lies in elaborating on five levels of a supply chain network catering to the delivery of multiple products across various periods. Another novelty is the simultaneous incorporation of economic, environmental, and reliability objectives in the network design—a facet rarely addressed in prior research. Results highlight that varying demand levels for each facility lead to altered trade-offs between objectives, empowering practitioners to make diverse decisions in facility location allocation. The proposed mathematical model undergoes validation through numerical examples and sensitivity analysis of parameters. The paper concludes by presenting theoretical and managerial implications, contributing valuable insights to the field of sustainable supply chains.

Purpose: Maintenance dredging can often hinder port operations resulting in waiting times for seagoing vessels. The purpose of this paper is to investigate the dynamics between maintenance dredging activities and seagoing vessels, specifically focusing on how waiting times can be reduced. Then, the role of selecting different maintenance dredging strategies in reducing these waiting times is outlined. Methods: The study analyzes historical automatic identification system (AIS) data to identify the interaction between maintenance dredging and seagoing vessels and quantify the hindrance periods for the Mississippihaven case study in the Port of Rotterdam, the Netherlands. The trajectories of the vessels are analyzed in a simple case to show how the vessels interact and how the waiting times are quantified. The interactions are checked with the Port of Rotterdam for different port calls to ensure that maintenance dredging was the reason for these delays. Results: By analyzing the AIS data analysis of vessels in a given time window, the dredgers for maintenance work can be identified and their activities within or near the terminal can be determined. In addition, the waiting time of the seagoing vessel caused by the maintenance dredging is quantified at the terminal entrance. Conclusion: The study discusses how the maintenance dredging operations could be improved by adjusting the loading and sailing phases of maintenance dredging and provides some theoretical and managerial insights. Alternative port maintenance strategies to minimize the waiting time caused by the hindrance are also discussed.

In the original publication of the article first author name has been misspelled as “Morteza Sheykhzadeh”. The correct name is “Morteza Sheykhizadeh”. The original article has been corrected.

Due to the outbreak of COVID-19 around the globe in the last few years, the need for pharmaceutical supply chains is felt more than before. However, increasing uncertainties along with unpredictable demand for products led to disruptions in supply chains when receiving requests from retailers. These disruptions not only affected the economic aspect of supply chains but also caused shortages in hospitals and medical centers. Therefore, it has become significant for companies to select their suppliers to avoid disruptions in the case of the severity of infections. To address this issue in practice, this paper has been conducted based on a case study to address the role of lean, agile, resilience, and green (LARG) criteria in selecting the supplier in a pharmaceutical supply chain and compare the results obtained before and after the prevalence of COVID-19. The main purpose of this study is to determine and evaluate different indicators within the LARG concept to avoid disruptions when selecting suppliers. Besides, the significance of these criteria before and after the pandemic condition is addressed. Due to addressing multiple aspects of the problem, a hybrid fuzzy multi-attribute decision-making (MADM) approach is adopted for this elaboration when the four LARG criteria are integrated with eighteen supplier selection sub-criteria. To calculate the impact of each criterion (or sub-criteria), a fuzzy best–worst method (BWM) along with an additive ratio assessment (ARAS) is employed to propose a supplier ranking for a distributor of a pharmaceutical supply chain. The developed model is novel as LARG criteria in the context of supplier selection have not been studied to address the disruptions in the pharmaceutical supply chain. This is significant because it gives insight to both retailers and suppliers to emphasize the correct criteria, especially in the pandemic or related disrupting conditions. The results demonstrated that quality, collaboration, safety stock, and environmental criteria weigh the highest before the pandemic, while just-in-time delivery, lead time, safety stock, and environmental criteria weigh the highest after the pandemic. This study demonstrates that developing a supplier selection approach that meets the demand in a short time and recommends suppliers to hold surplus inventory helps the healthcare systems better respond to the market needs.

Due to proofing error many corrections were overlooked by typesetter. Original article has been corrected.