challenge
For a global transportation & logistics client, predicting pickup volumes for their highest-tier and most urgent next day air delivery service was a challenge due to fluctuations in customer demand, and was reliant on human experience and interventions to improve, with no data-driven insights. Without precise predictions, planners would either over-allocate or under-allocate resources, negatively impacting our client’s efficiency and their customers’ satisfaction. They needed a partner to help them find a more reliable way to assist their route planners in optimizing daily dispatch schedules.
solution
We knew that AI tooling would be key to helping our client create more accurate predictions, so we focused on building a machine learning model in Google Cloud Platform (GCP) Gemini to predict pickup volumes based on historical trends. We deployed this solution using our unique development methodology to quickly produce results. The solution analyzed similarities in past pickup behavior, modeled probabilities, and forecasted expected pickups for specific routes on different days of the week.
results
The implementation of AI-generated routing recommendations has provided planners with valuable insights to enhance their decision-making and efficiency. By improving the accuracy of probability and quantity pickup predictions, the pilot allows for optimization of route planning; saving the company money on fuel and time expenses, and contributing to their goals of reducing their overall carbon footprint. Additionally, this initiative has established a company-first robust AI architecture, becoming a reference architecture for future GCP projects, and expanding innovative opportunities to leverage AI to solve additional business challenges in scalability and logistics optimization.
tech used
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