Currently, there is still a strong trend in research and in the market connected with the role of 3PL (third-party logistics) companies and the trend of developing and creating coordination in logistics networks. The most important issue for the following paper is the examination of the 3PL enterprise, which is able to create the demand forecasts to gain the functions of logistics coordination in the fields of inventory management and transportation planning. This research paper focuses on showing the demand forecasting tool results for 29 chosen distribution networks (in this paper, the traditional forecasting methods were used based on time series exponential smoothing, ARIMA, machine learning and neural-network-based methods, created in the R programming environment). In the next steps, the forecasting results were compared in the conditions of transportation planning and inventory management (in the conditions of “future” dynamic ABC analysis). The forecasting function supports the inventory management and transportation planning activity of 3PL as a key element of logistics coordination in distribution networks. The proper way to transfer the results of forecasting to an ABC analysis and transportation planning concept is to create a cloud-based system supported by data from the WMS (warehouse management system), while providing the possibility of results visualization by using some BI (business intelligence) solutions or different tools to create managerial information dashboards. Currently, one of the most efficient models connected with logistics coordination is the centralized network with 3PL responsible for planning and executing logistics processes with the creation of additional value. As such, 3PL is able to create a similar forecast for different types of DN and with different aggregations (per SKU or per recipient). It could support coordination in DN from the point of view of inventory management and transportation planning.

(1) Background: Currently, logistics operators are struggling with the problem of acquiring new areas to create added value. One of such challenges is acquiring a new function in the form of demand forecasting and coordination skills. The increase in the need to forecast demand is related to the increasing complexity of the distribution network, omnichannel systems and turbulent environment. It is necessary to have a comprehensive approach to the distribution network and to develop competences related to coordination. For the authors, one of the most important coordination factors is the quality of forecasts, especially in relation to modern logistics operators. (2) Methods: In literature studies, the authors combine prognostic abilities with a predisposition to coordinate logistic processes in distribution networks. The aim of the research is to develop a forecasting model that will allow a logistic operator who aspires to coordinate logistics processes to create forecasts in the distribution system. The tool was developed in the R programming language and allows for forecasting based on data from the Warehouse Management System; (3) Results: The quality of forecasts is correlated with the characteristics of the products, the relationship between the manufacturing company and the logistics operator, and the configuration of the distribution network in individual chains. The results of the forecasts were compared with selected features of each of the 5 distribution networks that were tested. In parallel, the attributes of a company capable of forecasting in the distribution network were analyzed. These attributes were also compared to those of the logistics operator. (4) Conclusions: The authors proved that a logistic operator with a set of defined features is capable of generating demand in the entire distribution network.

The paper deals with the concept of centralized demand forecasting and logistical coordination in distribution networks. The aim of the paper is to relate the results provided by the forecasting tools to the basic aspects of logistical coordination. The case of 29 distribution networks in which a logistics operator (3PL) operates and provides contract logistics services to a manufacturing company is analysed. The paper partially confirms the hypothesis of better testability of forecasts based on machine learning algorithms and artificial neural networks for demand planning by the logistics operator to the manufacturer in the framework of logistics coordination in the distribution network. These algorithms perform better for networks with high specificity of flows and food networks. Traditional algorithms, on the other hand, have their better share in creating forecasts for more standard distribution networks. Additionally, the second hypothesis regarding the positive influence of modern technological solutions (such as the use of cloud technologies, EDI and flow tracking standards) was confirmed. Additionally, a number of factors that did not have a direct impact on forecasting errors were detailed.

This article addresses the key and current issues of smart cities in the context of last-mile supply management. Specifically, it explores how third-party logistics (3PL) activities impact last-mile delivery management in smart cities. It examines how 3PL affects delivery volumes, expanding the predictive capabilities of logistics operators. A research question included in the Introduction of this paper is also posed to explore the problem in depth. The research conducted focuses mainly on a case study conducted on the operations of an international 3PL logistics operator. In addition, predictive methods are used to analyse the shipment volume data for individual barcodes in the two analysed cities in Poland. Currently, the concept of a smart city assumes the limited participation of logistics operators in creating improvements for cities. The case study analysis shows that in the cities studied, 3PL companies, through predictive actions, can regulate the flow of vehicles out of the logistics centre and into the city, thus influencing the traffic volume in the city. The research is limited to two cities in Poland implementing smart city solutions and one logistics operator. The research also does not include e-commerce. The authors acknowledge that the results obtained cannot be generalised to a larger scale. This paper bridges the research gap on 3PL activities for last-mile logistics improvements. In addition, the paper proposes the first concept related to the implementation of a 3PL company’s predictive activities associated with the operator’s ability to control the impact on urban traffic.