Demand forecasting is a fundamental component of efficient supply chain management. An accurate demand forecast is required at several different levels of a supply chain network to support the planning and decision-making process in various departments. In this paper, we investigate the performance of bottom-up, top-down and optimal combination forecasting approaches in a supply chain. We first evaluate their forecast performance by means of a simulation study and an empirical investigation in a multi-echelon distribution network from a major European brewery company. For the latter, the grouped time series forecasting structure is designed to support managers’ decisions in manufacturing, marketing, finance and logistics. Then, we examine the forecast accuracy of combining forecasts of these approaches. Results reveal that forecast combinations produce forecasts that are more accurate and less biased than individual approaches. Moreover, we develop a model to analyse the association between time series characteristics and the effectiveness of each approach. Results provide insights into the interaction among time series characteristics and the performance of these approaches at the bottom level of the hierarchy. Valuable insights are offered to practitioners and the paper closes with final remarks and agenda for further research in this area.

Decision making in city logistics (CL) is complex due to the numerous concepts and alternatives, as well as the intricate relationships between measures and effects. This study introduces a novel approach to evaluating urban freight transport (UFT) by combining multi-criteria decision making (MCDM) and social cost–benefit analysis (SCBA). This combination aims to improve decision making for sustainable CL concepts, particularly in reducing externalities in last-mile delivery. The model assesses various CL initiatives and urban consolidation center (UCC) concepts for their impact on UFT externalities. It uses the MCDM for ex ante scenarios assessment and prioritization. Input data were collected through a survey of experts from various sectors, and the Analytic Hierarchy Process (AHP) was applied in the case study of Novi Sad, Serbia. The prioritization highlighted the significance of implementing restrictive regulatory measures, alternative transport modes, and operational optimization within UCC concepts. By estimating capital, operational, and external costs, SCBA was applied to the prioritized UCC concepts, which were then further evaluated using the SCBA outputs. Sensitivity analysis was employed to assess the robustness of the proposed model. This paper offers valuable insights into the potential use of existing tools within a hybrid model to enhance decision making in CL.

Sustainable development, urban planning, mobility, and transport planning, integrated within the context of sustainable urban mobility, have been central themes in both scientific and applied spheres over the past few decades. In port cities, it becomes particularly essential to tackle sustainability issues given the pollution and noise emanating from ships and other port-related activities. To meet mobility and transportation sustainability needs in the port area, a port should implement measures aligned with a sustainable urban mobility planning (SUMP) approach. However, many ports have thus far achieved limited results in this direction due to the absence of an approach to defining sustainable mobility solutions based on the SUMP approach for an urban area associated with the given port. The overall aim of this paper is to support the development of territorial SUMP for port areas by proposing a methodology that identifies and prioritizes sustainable mobility solutions tailored to a specific port area. The proposed methodology is applied in the Port of Bar (Montenegro) through an appropriate case study. In this case study, the methodological steps are systematically followed, resulting in the practical implementation of the selected mobility solution: the use of a hybrid bus for internal employee transportation within the port area. The undertaken case study underscores the simplicity, practical applicability, and adaptability of the proposed methodology.

Technologies and technological concepts of Industry 4.0 are transforming business, reshaping business processes, and redefining employee competencies. In the era of digitalization, the vast amount of data generated daily renders traditional knowledge and approaches insufficient for managing logistics and supply chains, making continuous education imperative. This paper is the result of broader research conducted within a joint project involving five European higher education institutions specializing in logistics. The project's primary objective was to identify and develop analytical skills as a key component of the broader competency framework required for logistics and supply chain managers. The research was conducted in multiple stages, incorporating a critical literature review on logistics competencies, an analysis of logistics study program curricula, and an online survey targeting university teachers, students, and logistics professionals. The findings - namely, the systematization of analytical skills and their integration into three proposed educational courses - along with their implementation strategies, contribute to bridging the gap between the existing competencies of logistics and supply chain managers and those required in the era of digitization.