Search Results (4)

The healthcare industry faces mounting pressure to enhance efficiency and accuracy in logistics operations. Despite its critical role, the sector demonstrates a low adoption rate of logistics automation, with the investment ratio at 14.9%, significantly lower than the industrial average of 18%. This study explores the current state and strategic application of logistics automation in healthcare through 20 in-depth interviews with stakeholders across manufacturers, wholesalers, hospitals, clinics, and pharmacies in South Korea. Analysis revealed that automation adoption is largely contingent on two key factors: annual order volumes and inventory complexity. Companies handling over 100,000 order lines annually and managing over 1000 SKUs were more likely to have adopted or planned automation systems such as AS/RSs, AMRs, or Cube-based AS/RS. The research culminates in a directional map that aligns automation strategies with operational scale and product characteristics. This study contributes novel empirical insights into the fragmented healthcare logistics sector, offering actionable guidance for phased automation implementation based on contextual constraints and stakeholder typologies. Full article

Background: Artificial intelligence (AI) and robotics are increasingly pivotal for reducing carbon emissions in supply chain management (SCM); however, research exploring their combined potential from a sustainability perspective remains fragmented. This study aims to systematically map the research landscape and synthesize evidence on the applications, benefits, and challenges. Methods: A systematic scoping review was conducted on 23 peer-reviewed studies from the Scopus database, published between 2013 and 2024. Data were systematically extracted and analyzed for publication trends, application domains (e.g., transportation, warehousing), specific AI and robotic technologies, emissions reduction strategies, and implementation challenges. Results: The analysis reveals that AI-driven logistics optimization is the most frequently reported strategy for reducing transportation emissions. At the same time, robotic automation is commonly associated with improved energy efficiency in warehousing. Despite these benefits, the reviewed literature consistently identifies significant barriers, including the high energy demands of AI computation and complexities in data integration. Conclusions: This review confirms the transformative potential of AI and robotics for developing low-carbon supply chains. An evidence-based framework is proposed to guide practical implementation and identify critical gaps, such as the need for standardized validation benchmarks, to direct future research and accelerate the transition to sustainable SCM. Full article

Intelligent manufacturing workshop uses automatic guided vehicles as an important logistics and transportation carrier, and most of the existing research adopts the intelligent manufacturing workshop layout and Automated Guided Vehicle (AGV) path step-by-step optimization, which leads to problems such as low AGV operation efficiency and inability to achieve the optimal layout. For this reason, a smart manufacturing assembly line layout optimization model considering AGV path planning with the objective of minimizing the amount of material flow and the shortest AGV path is designed for the machining shop of a discrete manufacturing enterprise of a smart manufacturing company. Firstly, the information of the current node, the next node and the target node is added to the heuristic information, and the dynamic adjustment factor is added to make the heuristic information guiding in the early stage and the pheromone guiding in the later stage of iteration; secondly, the Laplace distribution is introduced to regulate the volatilization of the pheromone in the pheromone updating of the ant colony algorithm, which speeds up the speed of convergence; the path obtained by the ant colony algorithm is subjected to the deletion of the bi-directional redundant nodes, which enhances the path smoothing degree; and finally, the improved ant colony algorithm is fused with the improved dynamic window algorithm, so as to enable the robots to arrive at the end point safely. Simulation shows that in the same map environment, the ant colony algorithm compared with the basic ant colony algorithm reduces the path length by 40% to 67% compared to the basic ant colony algorithm and reduces the path inflection points by 34% to 60%, which is more suitable for complex environments. It also verifies the feasibility and superiority of the conflict-free path optimization strategy in solving the production scheduling problem of the flexible machining operation shop. Full article

The COVID-19 pandemic has led to major disruptions in workflows across all industries. All sectors are trying to sustain operations during this extremely difficult time and the healthcare sector is the most important of them. It is unthinkable to stop the operations of the health system because it serves human life. Health institutions must supply the products such as masks, gloves, and ventilators subject to service on time for certain activities to continue indefinitely under all conditions. By adopting modern logistics activities and technologies, healthcare organizations can provide sustainable diagnosis and treatments to patients by automating their various operations. With the COVID-19 pandemic, how to select an appropriate sustainable supplier has become an important task in the era of Logistics 4.0. From this viewpoint, a sustainable supplier selection framework is implemented for a health institution under the effect of the pandemic. To determine the direct effects of the pandemic in the health sector, fuzzy Multi-Criteria Decision-Making (MCDM) methods are utilized in the application. After a thorough review of the literature and interviews with experts, the criteria are organized in a comprehensive hierarchical structure. The fuzzy Best-Worst Method (F-BWM) technique is employed to find the weights for the determined criteria. Consequently, the fuzzy Additive Ratio Assessment Method (F-ARAS) method was applied to rank the alternative suppliers. In addition, with a comprehensive sensitivity analysis, alternative situations are examined against possible breaks in the supply chain. Thus, from the perspective of Logistics 4.0 and sustainability, this study contributes to the literature with an analysis of the health system’s survival in difficult and fragile periods, such as COVID-19. Investigating the importance of SSS can be a road map for the policymakers and the decision-makers is beneficial since the impact of COVID-19 on SSS is studied from the perspective of Logistics 4.0. Full article