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In recent years, the complexity of last mile freight transportation management has increased. Shippers are facing demand fragmentation and often rely on a set of external carriers for the delivery to customers. Different carriers are characterized by different costs, service level and range of delivery services. Therefore, selecting the right carrier for each delivery is crucial for shippers' efficiency and effectiveness. However, research has largely overlooked this issue. This study represents a first attempt to address the carrier selection problem through the application of machine learning. Considering the real-world data of a shipper, a machine learning model is developed and its application is simulated to assign each delivery to the most suitable carrier based on delivery features and carriers' historical lead time performance. Results show that significant performance improvements can be achieved compared with the approach currently used by the shipper, highlighting the potential for further research in this area. [ABSTRACT FROM AUTHOR]

The mass customisation strategy is needed by manufacturing companies to face the increasing variety and unpredictability of products required by customers. However, mass customisation may increase the complexity of managing manufacturing and production logistics activities, for example due to reduced product batch sizes. The synchronisation of material flows within the factory is emerging as a way to address this complexity, as it enables an effective and efficient implementation of mass customisation. Indeed, the fourth Industrial Revolution introduces new digital levers, which can be combined with traditional managerial levers to achieve the synchronisation of material flows within the factory. This study contributes to the rising stream of research on this topic. A systematic literature review was conducted, leading to the development of a classification framework of the levers supporting the synchronisation of material flows. The identified managerial levers are: storage of materials, feeding policy, and scheduling. The digital levers are: materials tracking, process tracking, data analytics, and assistance systems. The developed framework was operationalised in four industrial cases and applied as a tool to map their levers related to the synchronisation of material flows. [ABSTRACT FROM AUTHOR]