Version 1
: Received: 12 September 2024 / Approved: 12 September 2024 / Online: 12 September 2024 (15:53:20 CEST)
How to cite:
Wolny, M.; Kmiecik, M. Analizying Forecasting Errors in 3PL Activity: A Case Study on Distribution Channels Using Stl Decomposition. Preprints2024, 2024091003. https://doi.org/10.20944/preprints202409.1003.v1
Wolny, M.; Kmiecik, M. Analizying Forecasting Errors in 3PL Activity: A Case Study on Distribution Channels Using Stl Decomposition. Preprints 2024, 2024091003. https://doi.org/10.20944/preprints202409.1003.v1
Wolny, M.; Kmiecik, M. Analizying Forecasting Errors in 3PL Activity: A Case Study on Distribution Channels Using Stl Decomposition. Preprints2024, 2024091003. https://doi.org/10.20944/preprints202409.1003.v1
APA Style
Wolny, M., & Kmiecik, M. (2024). Analizying Forecasting Errors in 3PL Activity: A Case Study on Distribution Channels Using Stl Decomposition. Preprints. https://doi.org/10.20944/preprints202409.1003.v1
Chicago/Turabian Style
Wolny, M. and Mariusz Kmiecik. 2024 "Analizying Forecasting Errors in 3PL Activity: A Case Study on Distribution Channels Using Stl Decomposition" Preprints. https://doi.org/10.20944/preprints202409.1003.v1
Abstract
Purpose:
Decomposition analysis of forecasting errors relating to time series generated by a 3PL logistics operator for ten distribution channels operated by the logistics operator.
Design / methodology / approach:
The studies were focused on the analysis of 10 distribution channels operated by the 3PL logistics operator who used a forecasting tool based on a modified ARIMA algorithm to prepare forecasts. In this paper, R environment was used. The studies focused on the visual analysis of forecasting error series, on the analysis of the basic parameters of the error time series distributions, on the analysis of STL decomposition and statistical tests relating to trend and seasonality.
Findings:
The forecasting error analysis indicates that there are different patterns and characteristics of errors for individual channels. The statistical test results for various channels display significant differences between forecast groups in some cases. This suggests that the forecasting tool can be more accurate for some than for other channels.
Research limitations:
Logistic operations are usually based on numerous variables which may influence forecast quality. Moreover, the absence of any information on the forecasting models and input data used may prevent complete understanding of error sources.
Value of the paper:
The studies described in this paper emphasized valuable conclusions which can be drawn from the analysis of time series forecasting errors in the context of logistic operations. The findings indicated the need for an adapted approach to forecasting for each and every channel, the importance of improving the forecasting tool and the potential to optimize the forecast accuracy by focusing on the trend and seasonality. For this reason, the analysis is an important input into the theory and practice relating to demand forecasting by logistics operators in distribution networks. The studies contribute to the works related to demand forecasting by logistics operators.
Keywords
time series of forecasting errors; 3PL; logistics operator; demand forecasting; distribution channels
Subject
Business, Economics and Management, Econometrics and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
