Forwarder selection - price prediction - machine learning - logistics optimization - TMS-integration
One of our customers is a large logistics service provider that manages order fulfillment within BeNeLux and Germany. For orders that need to be shipped outside the BeNeLux region, they must select a suitable forwarder from their network of partner companies. Currently, this process is mostly manual and heavily dependent on the experience and tacit knowledge of the staff. Identifying the right partners, requesting quotes, and waiting for responses is time-consuming, making it difficult to generate quotations for customers in just a few minutes as desired.
There is significant potential for improvement, as we have access to extensive historical data on previous quotations, partner pricing for specific destinations, and records of which forwarders were ultimately chosen. By analyzing these historical transactions, along with current market conditions and the particular requirements of each order (such as weight and dimensions), it becomes possible to automate the selection and pricing process.
The objective of this assignment is to develop an AI-based model that can identify the most suitable forwarder and predict the likely price for shipments outside the BeNeLux and Germany. The model should incorporate information such as historical pricing trends, market fluctuations, specific logistics requirements for each case, and performance indicators of the forwarders, like reliability and delivery times.
Throughout the assignment, we can help you contact both logistics and IT staff at the company to ensure the solution is practical, aligns with business needs, and fits seamlessly into existing processes. Hands-on support and real-world feedback from end users will be key in refining your solution.
The end result should be a practical tool that recommends the optimal forwarder and generates accurate price estimates quickly, enhancing both efficiency and accuracy in the company’s quotation process. This will enable the company to provide more timely and precise quotes to its customers. The solution should be thoroughly validated, integrated into existing workflows, and delivered with a clear implementation plan for real-life use.
Get in touch with Martijn, founder of Bullit.
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