This paper presents a comprehensive review of machine learning (ML) and deep learning (DL) models used for demand forecasting in supply chain management. By analyzing 119 papers from the Scopus database covering the period from 2015 to 2024, this study provides both macro- and micro-level insights into the effectiveness of AI-based methodologies. The macro-level analysis illustrates the overall trajectory and trends in ML and DL applications, while the micro-level analysis explores the specific distinctions and advantages of these models. This review aims to serve as a valuable resource for improving demand forecasting in supply chain management using ML and DL techniques.

Urban logistics face growing complexity as cities seek sustainable development pathways; one solution is integrating freight transportation with public transit. The purpose of this study is to evaluate current practices, identify gaps, and highlight opportunities for advancement in this field. Through a comprehensive literature review guided by four research questions, this study evaluates methodologies, vehicle choices, the application of intermodal containers, and the shared use of public transportation infrastructure for freight delivery. A meticulous search methodology was employed to select references, which were then analyzed and classified into two main tables. Findings reveal a progression from initial pilot projects and cost analyses to sophisticated planning and optimization challenges, with a focus on metro networks and buses. This study uncovers a predilection for certain research types, the utilization of varied transportation modes, underexplored intermodal container use, and an emerging emphasis on transshipment operations. The integration of freight and public transportation within urban settings is identified as a dynamic and diversifying field, with significant potential for contributing to sustainable urban development. We outline nine areas for future research, including the incorporation of external costs and congestion effects into optimization models, and the exploration of unmanned vehicles, thereby laying the groundwork for more inclusive and efficient urban logistics solutions.

Over recent years, driven by intertwined economic, social, environmental, and technological factors, urbanization has accelerated at an unprecedented pace, posing complex challenges to metropolitan transport systems. This has intensified the demand for innovative mobility solutions, notably Mobility as a Service (MaaS), which promotes a paradigm shift from private vehicle ownership to mobility consumed as a service. With rapid advances in digital technologies, MaaS has gained substantial momentum, attracting significant scholarly attention for its potential to enable intelligent and sustainable transportation systems. This study aims to provide a comprehensive conceptual foundation of MaaS and its components, and to systematically examine how artificial intelligence (AI), machine learning (ML), and big data techniques are applied in this domain. Following PRISMA guidelines, a bibliometric and systematic review was conducted on peer-reviewed articles published between 2020 and 2024 and indexed in the Scopus and Web of Science databases. The analysis classifies AI applications across four MaaS integration levels: basic, intermediate, advanced, and full integration. The results show that machine learning and basic optimization dominate at the basic level; blockchain and big data are most prominent at the advanced and full levels; and deep learning is applied across all levels, with a particularly strong presence at the advanced stage for real-time, personalized mobility solutions. The findings also indicate that while most implementations focus on developed countries, there is substantial potential for adaptation in emerging markets. The paper concludes by discussing key challenges in regulatory compliance, inclusivity, and the protection of sensitive user data, and outlines future research avenues for building socially equitable, intelligent, and sustainable MaaS ecosystems.

Optimizing last-mile delivery operations is an essential component in making a modern city livable, particularly in the face of rapid urbanization, increasing e-commerce activity, and the growing demand for fast deliveries. These factors contribute significantly to traffic congestion and pollution, especially in densely populated urban centers like Casablanca. This paper presents an innovative approach to optimizing last-mile delivery by integrating public transportation into the logistics network to address these challenges. A custom-built environment is developed, utilizing public transportation nodes as transshipment nodes for standardized packets of goods, combined with a realistic simulation of traffic conditions through the integration of the travel time index (TTI) for Casablanca. The pickup and delivery operations are optimized with the proximal policy optimization algorithm within this environment, and experiments are conducted to assess the effectiveness of public transportation integration and three different exploration strategies. The experiments show that scenarios integrating public transportation yield significantly higher mean rewards—up to 1.49 million—and more stable policy convergence, compared to negative outcomes when public transportation is absent. The highest-performing configuration, combining PPO with segmented training and public transport integration, achieves the best value loss (0.0129) and learning stability, albeit with a trade-off in task completion. This research introduces a novel, scalable reinforcement learning framework to optimize pickup and delivery with time windows by exploiting both public transportation and traditional delivery vehicles.

Blockchain technology is expected to have a radical impact on most industries by boosting security, transparency, and efficiency. This work considers the potential benefits of blockchain-focused applications in industrial process monitoring. The research design facilitates a detailed bibliometric analysis and delivers insights into the intellectual structure of blockchain technology’s application in industry via scientometric approaches. The work also approaches numerous sources in various industrial sectors to identify the transformative role of blockchain in industrial processes. Aspects such as blockchain technology’s impact on industrial processes’ transparency are discussed, while the paper does not ignore that success stories in applying blockchain to industrial sectors are often exaggerated due to a highly competitive environment that the cryptocurrency domain has become. Finally, the work presents major research avenues and decision-making areas that should be tackled to maximize the disruptive potential of blockchain and create a secure, transparent, and inclusive future.

Background: Replenishment process is one of the success factors for a more efficient management of operations and a regained profitability. The application of the principles of lean manufacturing in the field of industrial logistics, where substantial improvements in the replenishment process are necessary, is relevant with the objective of optimising the replenishment flow of raw materials from assembly lines in order to propose and implement robust solutions capable of mitigating or even eliminating all types of waste and maximising the productivity of value-added activities. Methods: To achieve this goal, the paper follows the spirit of the DMAIC (Define, Measure, Analyse, Improve and Control) and 5-Why methods that allow the mapping of internal processes of factory logistics in the particular case of a Wire Harness Automotive Plant. Results: Based on the data collected, as well as on specific analysis methods, simulations are launched to challenge and validate the improvements made to the replenishment process. Conclusions: Several generalisable lessons are learnt regarding replenishment flow management, covering topics such as inventory traceability, in addition to logistics costs and process flow optimisation.