Efficiency improvement in asset-based freight transportation company

Abstract

The logistics industry operates cyclically, requiring companies, especially those with tangible assets, to harmonise their growth strategies with market fluctuations. This paper explores the impact of market cycles on asset-based companies, highlighting the challenges and opportunities they encounter during both low and high cycles. During low cycles, growth constraints can lead to resource underutilization, operating at suboptimal costs, and significant financial setbacks. Conversely, high cycles often bring challenges related to delayed readiness and resource scarcity, hindering companies from fully capitalising on market opportunities and achieving strategic objectives promptly. By employing predictive models for demand and freight rate forecasting, companies can proactively manage their resources and investments to navigate through market fluctuations and gain a competitive edge. Leveraging digitisation and data-driven approaches presents new avenues for enhancing operational efficiency in the logistics sector. However, accurately forecasting demand and freight rates for full truckloads in road freight transportation remains a daunting task due to data complexity and variability. This paper seeks to tackle this challenge by proposing data-driven models to enhance effectiveness in the road freight transportation domain.