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Drop Trailer Forecasting in Volatile Networks: an Innovative Approach

EasyChair Preprint no. 13101

8 pagesDate: April 27, 2024


This paper delves into the critical aspect of demand forecasting within the broader context of optimizing drop trailer management in volatile networks, with a specific focus on a large pallet manufacturer’s (i.e. sponsor company) supply chain operation. The study underscores the importance of accurate demand forecasting as a foundational element for informing subsequent optimization models. The main objective is to enhance our sponsor company’s supply chain efficiency by accurately predicting future trailer requirements, which is crucial for the subsequent development of an effective inventory control and optimization model.

The research utilizes forecasting methods like Gradient Boosting (XGBoost) and highlights the challenges of traditional forecasting methods in the context of our sponsor company’s complex and dynamic supply chain network. The demand forecast is meant to inform the development of optimization models crucial for ensuring the effective allocation and management of trailer assets, ultimately leading to cost reductions and improved service levels within our sponsor company’s network. The study contributes significantly to supply chain management literature by showcasing the application of sophisticated forecasting techniques in a real-world context and setting the stage for the development of robust optimization models in the domain of drop trailer management.

Keyphrases: Demand Forecasting, Drop Trailer Management, Inventory Management, machine learning, Supply Chain Management, XGBoost

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Alex Carroll and Troy Egar},
  title = {Drop Trailer Forecasting in Volatile Networks: an Innovative Approach},
  howpublished = {EasyChair Preprint no. 13101},

  year = {EasyChair, 2024}}
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