<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

Supporting of manufacturer’s demand plans as an element of logistics coordination in the distribution network

Production Engineering Archives

This work is licensed under the Creative Commons Attribution 4.0 International License.

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...

Supporting of manufacturer’s demand plans as an element of logistics coordination in the distribution network

Data publikacji: 15 lut 2023

Zakres stron: 69 - 82

Otrzymano: 01 lis 2022

Przyjęty: 03 gru 2022

DOI: https://doi.org/10.30657/pea.2023.29.9

Słowa kluczowe
Logistics service provider, 3PL – Third Party Logistics, Logistics coordination, Distribution network, Demand forecasting

© 2023 Mariusz Kmiecik, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Supporting of manufacturer’s demand plans as an element of logistics coordination in the distribution network

Data publikacji: 15 lut 2023

Zakres stron: 69 - 82

Otrzymano: 01 lis 2022

Przyjęty: 03 gru 2022

DOI: https://doi.org/10.30657/pea.2023.29.9

Słowa kluczoweLogistics service provider, 3PL – Third Party Logistics, Logistics coordination, Distribution network, Demand forecasting

© 2023 Mariusz Kmiecik, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Logistics service provider, 3PL – Third Party Logistics, Logistics coordination, Distribution network, Demand forecasting