The purpose of this study is to show how the implementation of the Kanban system based on ABC/XYZ analysis helps in improving the inventory management system in the supply chain where there is a lack of proper inventory management. Ethiopian pharmaceutical supply agency's distribution network is taken as a case to show the significance of the Kanban system. Monthly data for one year is collected and is accompanied by primary data obtained from direct observation and discussion with the company's management staff. ABC, XYZ, and ABC/XYZ matrix analyses were performed to determine items to be considered in Kanban implementation. By analysis, it is shown that the Kanban system can improve inventory-related costs by about 75%. While the Kanban system is rarely implemented in the pharmaceutical sector so far, it is understood in this study that it can bring improvements in the sector if properly implemented. Though it is shown by analyzing data on how the Kanban system can improve inventory-related costs, it is imperative to practically implement it for validation of its effectiveness.

Street delivery faces significant challenges due to outdated road infrastructure, which was not designed to handle current vehicle volumes, leading to congestion and inefficiencies, especially in last-mile delivery. Integrating drones into the delivery system offers a promising solution by bypassing congested roads, thereby enhancing delivery speed and reducing infrastructure strain. However, optimizing this multimodal delivery system is complex and data-driven, with real-world data often being costly and restricted. To address this, we propose a synthetic dataset generator that creates diverse and realistic delivery scenarios, incorporating environmental variables, customer profiles, and vehicle characteristics. The key contribution of our work is the development of a dynamic generator for multiple optimization problems with diverse complexities or even combinations of optimization problems. This generator allows for the incorporation of real-world factors such as traffic congestion and synthetically generated factors such as wind conditions and communication constraints, as well as others. The primary objective is to establish a foundation for creating benchmark scenarios that enable the comparison of existing and new approaches. We evaluate the generated dataset by applying it to three optimization problems, including facility location, vehicle routing, and path planning, using different techniques to demonstrate the dataset’s effectiveness and operational viability.

This study presents a comprehensive supply chain performance optimization model that addresses the trade-off between supply chain cost and customer service level in the distribution network. The model incorporates both deterministic and scenario-based approaches, allowing for a more realistic representation of supply chain operations. The model is applied to a case company operating in the pharmaceutical supply chain in Ethiopia. The goal is to improve the company’s supply chain performance by optimizing various factors such as establishment costs, handling costs, transportation costs, and demand satisfaction. The study considers both financial measures (supply chain cost) and non-financial measures (customer service level) to evaluate the performance of the supply chain. The results of the study demonstrate the effectiveness of the proposed model in identifying the optimal trade-off between supply chain costs and customer service levels. By comparing the results of the model with the current situation of the case company, it is determined that the company can achieve significant cost reductions of up to 25.26% while still meeting customer demands. The model also takes into account the uncertainty in demand, providing more realistic recommendations for distribution center locations, transportation planning, and order fulfillment. The implications of using this optimization model include the potential for cost savings, improved decision-making, enhanced customer satisfaction, and, ultimately, a more successful supply chain. However, the study has some limitations, including a need for further research on other objectives and considerations, such as environmental impacts and disruptions, which could be addressed in future research directions.

A lot of attention has been paid to performance measurement during the past few decades. There are several reasons for utilizing performance measurement in a business, but the most important one is that, when done right, it will increase productivity. Productivity is crucial to a business’s capacity to compete and generate long-term profitability while providing required customer service. A firm will not last in the fierce business world of today if it cannot use its resources effectively to provide value for its consumers. This is especially important for public companies with limited public investments. The creation of adequate and completely usable performance assessment systems, or sets of measures, has, nevertheless, proven to be a very difficult process. This research has focused on the identification of key performance measures and metrics, on which there is still no common understanding. It focuses on identifying the outbound logistical aspect of the pharmaceutical supply chain’s performance measures and metrics. The study is mainly based on previous studies and opinions of the case company’s management staff. Based on a review of previous studies on and discussions with the management staff of the case company, performance measures for a pharmaceutical supply chain (focusing on inventory management and distribution activities) are identified and categorized into quality, time, financial, and productivity measures. Quality measures are observed to be the most crucial metric while assessing pharmaceutical supply chain performance. The case company’s management can utilize the study as a base to develop their own performance measurement system.

Background: The increasing integration of automated transport drones into logistics networks presents transformative potential for addressing contemporary logistics challenges, particularly in last-mile delivery, healthcare, disaster response, urban mobility, and postal services. However, their effective integration into varied logistics contexts remains hindered by infrastructure, regulatory, and operational limitations. This study aims to explore how drone-based logistics systems can be systematically planned and evaluated across diverse operational environments. Methods: A structured literature review was conducted, employing thematic synthesis to analyze current research on drone logistics. The analysis focused on identifying the key planning dimensions and interrelated components that influence the deployment of drone-based transport systems. Results: The review identified seven central planning dimensions: areas of application, system components, transport configuration, geographic areas, optimization and analysis methods, logistical planning, and performance assessment. These dimensions inform a conceptual framework designed to guide the planning and assessment of drone logistics networks. Conclusions: While existing studies contribute valuable insights into route optimization and drone deployment strategies, they often overlook integrative approaches that account for societal and environmental factors. The study emphasizes the need for interdisciplinary collaboration and context-specific planning frameworks to enhance the sustainable and effective implementation of drone-based logistics systems.