Increase business performance of a logistic player by using a probabilistic pricing engine

Reference Case

Client profile

Client: Logistics Service Provider
Industry: Logistics
Process: Bulk Transport Solutions

Our client is a logistics player and offers a web-based platform to match shippers and carriers for bulk transport. For each incoming shipment request, our client evaluates the requirements of the job and decides if they want to submit a quote and for which price. Once a shipment is granted, our client has to arrange transport capacity and posts the request on their platform for all eligible carriers. After negotiations, a deal is made with a carrier, which will take charge of carrying out the transport.

The problem

As part of its business model, our client receives the margin between the price paid by the shipper and the price at which the job is sold to the matched carrier. However, the current pricing strategy is susceptible to potential human bias and relies heavily on historical performance. Furthermore, notable price variations exist across the different transportation lanes. This poses a considerable risk to the profit margins.

Therefore, our client wants to improve its insights into the carrier prices, i.e., the minimum prices for which they can buy transport capacity from a carrier. These insights can be used to optimize the margins and to determine the relevance of incoming shipment requests.


To reduce these risk factors, our client aims to integrate AI-driven recommendations based on historical purchasing data and external market conditions. Through collaboration with the client’s subject matter experts, multiple hypotheses were formulated to assess the potential influence of various factors on the carrier price.

Subsequently, a carrier pricing model was constructed by combining the most impactful factors. In this process, several AI models were iteratively evaluated to accurately estimate the carrier price distributions. Here, each order involved the prediction of 9 prices corresponding to different degrees of confidence (10%, 20%, …, 90%) on price acceptance. We utilized Kedro for constructing a modular data science pipeline, managing tasks such as input data preprocessing, price estimating, and generating business outputs.

Solutions and added value

The initial AI model for the prediction of a single price already leads to a high performance, explaining 88% of the variation in carrier prices while accounting for prices differences across lanes. Rather than providing a single price prediction, the final delivered model generates 9 distinct prices allowing for a more informed decision-making process by evaluating the confidence in price acceptance using prediction reliability.

The model is to be integrated in the company’s applications, in order to facilitate (near) real-time assessment of incoming orders, and supporting the decision-making process for optimal pricing strategies. The impact of various factors on (individual) model predictions are described using explainable AI techniques to increase model interpretability & adoption by the end-users.



High performance AI model

explaining 88% of the variation in the carrier price

Reduce risks

by evaluating the confidence in price acceptance using prediction reliability

Enhanced trading strategy

leveraging live inference pipeline

Other projects

Case Study

Optimizing outbound supply chain design and processes to reduce transportation costs

Client: Curium Industry: Pharmaceutical industry Process: Logistic optimization
Case Study

Creating a prediction framework for solar energy production locations

Client: Covolt Industry: Solar energy Process: Asset management

Tell us your challenge.

We are here to help you.

Get in touch with our experts