- Demand Planning
- Machine Learning
Over the past several years, there has been severe market volatility in the truckload industry leading to cost increases and efficiency losses for shippers and carriers. Previous research has investigated the many factors that contribute to such market conditions. One topic that has yet to be analyzed is “ghost freight.” Ghost freight occurs either when no volume materializes on a lane (origin-destination pair) that was previously awarded to one or more primary carriers (a “full ghost” lane), or when the shipper tenders to only a subset of awarded primary carriers (a “partial ghost” lane). Our research leveraged five years of truckload market transactions for 15 shippers and over 300 carriers to conduct our analysis. We utilized Python and Tableau to identify and visualize the frequency of ghost freight across the market along with the types of lanes that tend to become ghost lanes. In addition, Ordinary Least Squares (OLS) regression was used to determine the impact of ghost freight on carrier performance. This research found that both full and partial ghost lanes occur very frequently in general each year, however there is a lack of pattern with respect to individual shipper behavior. The regression models did not show a clear impact of ghost freight on acceptance rates or prices. This may be the case in part because full ghost freight occurs overwhelmingly on low-volume lanes, which are traditionally not a capacity planning priority. That being said, we found that partial ghost lanes tend to occur on lanes that are often medium-to-high volume. This finding may be a topic of interest for carriers for future capacity planning. Further, although shippers do not appear to face direct financial repercussions resulting from carriers, it is ultimately inefficient to spend time and money awarding lanes that are never tendered to.