LAUSR

Purpose: The study aims to analyze forecast errors for various time series generated by a 3PL logistics operator across 10 distribution channels managed by the operator. Design/methodology/approach: This study examines forecasting errors across 10 distribution channels managed by a 3PL operator using Google Cloud AI forecasting. The R environment was used in the study. The research centered on analyzing forecast error series, particularly decomposition analysis of the series, to identify trends and seasonality in forecast errors. Findings: The analysis of forecast errors reveals diverse patterns and characteristics of errors across individual channels. A systematic component was observed in all analyzed household appliance channels (seasonality in all channels, and no significant trend identified only in Channel 10). In contrast, significant trends were identified in one pharmaceutical channel (Channel 02), while no systematic components were detected in the remaining channels within this group. Research limitations: Logistics operations typically depend on numerous variables, which may affect forecast accuracy. Additionally, the lack of information on the forecasting models, mechanisms (black box), and input data limits a comprehensive understanding of the sources of errors. Value of the paper: The study highlights the valuable insights that can be derived from analyzing forecast errors in the time series within the context of logistics operations. The findings underscore the need for a tailored forecasting approach for each channel, the importance of enhancing the forecasting tool, and the potential for improving forecast accuracy by focusing on trends and seasonality. The findings also emphasize that customized forecasting tools can significantly enhance operational efficiency by improving demand planning accuracy and reducing resource misallocation. This analysis makes a significant contribution to the theory and practice of demand forecasting by logistics operators in distribution networks. The research offers valuable contributions to ongoing efforts in demand forecasting by logistics operators.