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Predicting Shipping Time with Machine Learning
Antoine Jonquais & Florian KremplSCM Research Fest 2019, May 21st
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© 2019 MIT Center for Transportation & Logistics | Page 2
The presenters
Florian Krempl
Prior to coming to MIT, Florian worked at LKW Walter managing full truck loads for Amazon and Yusen Logistics all over the EU. In his Bachelor program at the University of Economics in Vienna, he specialized in Logistics and Finance.
Antoine Jonquais
Before being a student at MIT, Antoine worked as an account analyst at Hasbro and as a logistics project manager in France. He holds a Master’s degree in Logistics Engineering from ISEL - Université Le Havre Normandie.
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The sponsor company
A.P. Møeller-Mærsk A/S• Largest container shipping company in the world• Serves 343 ports worldwide, employs 80,000 people• Operates ships, terminals and tow-boats• Manufactures containers
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Agenda
§ Motivation
§ Model Description
§ Performance
§ Predicting in practice
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Motivation
Only 50% of ships arrive within 24h of their ETA
Schedule unreliability increases costs across the supply chains
90% of non-bulk cargo is transported by container
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Motivation
Transit time in days from Ningbo, China to Long Beach, CA
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Solution(s)
§ Best case scenario: deterministic transit times
§ Get more insights about actual transit times
§ Today, Maersk has a descriptive analytics tool: Harmony
§ Our project’s goal is predicting accurately when a
shipment will arrive
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Methodology
§ Identifying external factors that explain transit time variability
§ Converting historical data to predictor variables
§ Training machine learning models
§ Testing performance against real transit time
§ Build a prototype that predicts the transit time in real time
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Legs of the Journey
Port ofOrigin Route Port of
Destination
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Port of Origin Features
§ Expected Time to port§ Average time spent at Port of origin§ Standard deviation of time spent at Port of origin§ Origin Service§ Holiday§ Goods received late§ Late departure
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Route
§ Schedule
§ Average time spent per Route
§ Seasonal Variability
§ (Weather)
§ (Stops on the Route)
§ (Type of Vessel)
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Port of Destination
§ Capacity of port of destination
§ Average time spent at Port
§ Standard deviation of time
spent at Port
§ (Position of the container on the vessel)
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Model Overview
Booked Received Gate-in Depart
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Comparison of Metrics
BaselineNeural NetworkRandom Forest
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Input Model Booked
§ Booking Date
§ Carrier
§ Shipper
§ Route
§ Expected time of receipt
§ Origin Service
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Output
§ Route
§ Carrier
§ Shipper
§ Earliest Date of Arrival
§ Expected Date of Arrival
§ Latest Date of Arrival
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Conclusion
§ Machine Learning is a valid use to tackle this issue
§ Adding new selected features to the model could
improve its accuracy
§ Next steps for Maersk is to couple our prototype with
Harmony
§ Transition from descriptive analytics to predictive analytics
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Thank you for your attention