Price valuation modeling of less-than-truckload (LTL) shipments for financial continuity assurance

Abstract

PURPOSE: The aim of the research is to develop a mathematical model for LCVs that carry LTL loads. The model considers the intensity of vehicle usage and the number of loading and unloading points.

DESIGN/METHODOLOGY/APPROACH: This article presents an analysis of the fulfilment of LTL orders in 7 transport companies. The dataset covers 2 years and describes the use variables of 24 different N1 category vehicles with a maximum permissible gross weight of 3.5 tones. A set of indicators was used to assess the intensity of vehicle use and a statistical analysis was performed. Based on a dataset of technical and operational parameters describing the execution of LTL orders a multivariate regression model was developed to determine the value of LTL orders. Variables with a significant impact on the value of a transport order were selected. The estimation of regression parameter values was carried out based on the least squares method.

FINDINGS: Due to the specificity of LCV transport and the lack of necessity for monitoring them, research in this area is challenging and data are less frequently available. The developed model can be a practical tool for determining the value of LTL orders for LCV carriers.

PRACTICAL IMPLICATIONS: This paper presents a tool that carriers can use to assess the efficiency of their LTL orders. In practice, the tool can form part of a strategy that will support companies in securing financial continuity, creating healthy competition and industry standards.

ORIGINALITY/VALUE: Transport modeling primarily focuses on minimizing costs or maximizing profits. Available studies primarily present optimization models for Heavy Goods Vehicles (HGV). The literature does not offer a financial continuity model for Less Than Truckload (LTL) transport aimed at Light Commercial Vehicle (LCV) carriers. This paper addresses a gap in the literature by proposing a specialized reference a multivariate regression model specifically to the transport sector.