An Analogy Based Method for Freight Forwarding Cost Estimation

TL;DR

This paper evaluates an analogy-based cost estimation method for international freight forwarding using a k-NN algorithm, comparing its accuracy and practicality to manual estimation through simulations and real data.

Contribution

It introduces an analogy-based estimation approach using k-NN for freight costs and assesses its accuracy and labor efficiency compared to manual methods.

Findings

The analogy method's accuracy is comparable with minimal training.

It becomes more cost-effective than manual estimates when labor exceeds 1.5 hours.

The method's accuracy can be improved with better sampling policies.

Abstract

The author explored estimation by analogy (EBA) as a means of estimating the cost of international freight consignment. A version of the k-Nearest Neighbors algorithm (k-NN) was tested by predicting job costs from a database of over 5000 actual jobs booked by an Irish freight forwarding firm over a seven year period. The effect of a computer intensive training process on overall accuracy of the method was found to be insignificant when the method was implemented with four or fewer neighbors. Overall, the accuracy of the analogy based method, while still significantly less accurate than manually working up estimates, might be worthwhile to implement in practice, depending labor costs in an adopting firm. A simulation model was used to compare manual versus analytical estimation methods. The point of indifference occurs when it takes a firm more than 1.5 worker hours to prepare a manual estimate (at current Irish labor costs). Suggestions are given for future experiments to improve the sampling policy of the method to improve accuracy and to improve overall scalability.