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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. [ABSTRACT FROM AUTHOR]

Demand forecasting is one of the key activities in planning the freight flows in supply chains, and accordingly it is essential for planning and scheduling of logistic activities within observed supply chain. Accurate demand forecasting models directly influence the decrease of logistics costs, since they provide an assessment of customer demand. Customer demand is a key component for planning all logistic processes in supply chain, and therefore determining levels of customer demand is of great interest for supply chain managers. In this paper we deal with exactly this kind of problem, and we develop the seasonal Autoregressive IntegratedMoving Average (SARIMA) model for forecasting demand patterns of a major product of an observed beverage company. The model is easy to understand, flexible to use and appropriate for assisting the expert in decision making process about consumer demand in particular periods.

Inland waterway transport represent environmentally friendly transport mode accompanied with other potential benefits, such as increased transport safety and increased cost savings. In order to encourage further development of inland waterway transport in this paper focus is set on the identification of the barriers that limit and prevent development of inland waterway transport. More specifically, emphasis is placed on the barriers that are preventing the cargo shifting from land to waterway transport system. Research conducted in case of Republic of Serbia investigates several barriers which are classified in four main categories: goods, logistics, infrastructure, framework conditions. Framework conditions barriers are further subdivided and classified in four subcategories: political/legal, environment, economy, technology. Results indicate that the most significant negative influence on the further development of the inland waterway transport in Serbia comes from the political/legal barriers. This result implies that the government need to pay more attention to development of inland waterway transport, as well as to make more effort to enhance the cooperation with stakeholders.