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Intelligent Mobility: The Added Value of Predictions for Transport Delivery

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The complete logistics process from planning to delivery consists of many small decisions and many of those decisions are still made by humans. This session presents how better predicted data and automation with the Simacan platform allows shippers and carriers to be more efficient.

Speaker: Silvie Spreeuwenberg, Business Controller at Simacan.

*Intelligent Mobility 2021: Virtual Conference.

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Intelligent Mobility: The Added Value of Predictions for Transport Delivery

  1. 1. The added value of predictions for transport delivery A step towards continuous transport planning and 5-star delivery experience. Silvie Spreeuwenberg
  2. 2. ■ challenges in transport logistics ■ introduction to the Simacan platform ■ benefits - related to role of predictions ■ future of transport logistics ● autonomous shipments ● continuous planning Agenda
  3. 3. People on this session Business Controlling & Strategy Business controller and program manager for the scale-up program of Simacan. Passionate about using AI to improve and optimize human decision making. Silvie Spreeuwenberg
  4. 4. Legenda Supplier Inbound function depot Outbound function depot Receiver Journey Supplier Inbound depot Journey Inbound Outbound depot Journey Outbound Receiver strictly confidential - sharing this without Simacan consent is prohibited CONNECTING THE DOTS IN SUPPLY CHAIN Road transport by commercial (electric) vehicles
  5. 5. MANAGE THE PARADOX IN SUPPLY CHAIN! CUSTOMER INTIMACY OPERATIONAL EXCELLENCE BUSINESS NATURE CUSTOMER EXPECTATION
  6. 6. YOUR PLAN REALITY
  7. 7. The Simacan smart collaboration platform allows stakeholders in time critical delivery networks to provide customers with a 5-star delivery experience against minimal waste
  8. 8. Working as one! Some examples to start with
  9. 9. Working as one! DC - shop - driver - planner alignment
  10. 10. Working as one! Customer notification and proof of delivery
  11. 11. Working as one! eCMR paperless process, monitoring and alerting seamlessly integrated
  12. 12. Working as one! Departure and arrival specific routing instructions brought to existing onboard computers
  13. 13. Working as one! Business analist Order & Transport admin All data available for analytics & reporting
  14. 14. Working as one! Automated exception alerts and management
  15. 15. Shop manager Consumer Hub manager 9,9 5-STAR DELIVERY EXPERIENCE
  16. 16. Personal devices Personal devices Planning screen In & outbound monitor Onboard devices Platform intelligence enriches data for smarter exchange Intelligent algorithms Up-to-date maps Planning & routing Traffic information Intelligent traffic systems Master data Two-way communication Real-time insights Last-mile assistance Spot-on notification Home delivery supply chain
  17. 17. Benefits our clients report Role of predictions in managing the delivery experience
  18. 18. “Brilliant in its simplicity and reducing turnaround times with 15% on one hand and reducing the drivers time spent in traffic jams with 7%”
  19. 19. Current time & state ETA next stops Exception 11:00 Departing 12:00 - 13:30 none
  20. 20. Current time & state ETA next stops Exception 11:00 Departing 12:00 - 13:30 none 11:05 Driving 12:01 - 13:30 none
  21. 21. Current time & state ETA next stops Exception 11:00 Departing 12:00 - 13:30 none 11:05 Driving 12:01 - 13:30 none 11: 20 Driving slow traffic 12:05 - 13:35 minor delay
  22. 22. Current time & state ETA next stops Exception 11:00 Departing 12:00 - 13:30 none 11:05 Driving 12:01 - 13:30 none 11: 20 Driving slow traffic 12:05 - 13:35 minor delay 11:30 Not driving 13:30 - 15:30 major delays with consequences for subsequent deliveries
  23. 23. Current time & state ETA Exception 13:30 Onloading 15:30 none 13:40 Departing 15:30 none 13:50 Driving slow traffic 15:30 none predicted 15:30 Arriving 15:30 none predicted
  24. 24. Trajectory method We calculate the travel time from a starting date/time for one link in the route and use the resulting end time as the input for the next link. The travel time is based on speed profiles, actual free-flow speed, truck factors, region and date factors. Instantaneous method For each link in the route, we calculate the travel time using speed profiles, actual free-flow speed, truck factors and date factors. The travel times of each link are combined into one travel time. Truck factors The vehicle type influences the actual travel time in the case of certain roads or certain road elements such as roundabouts. Regional factors There may be regional differences between actual speeds per road type. Date factors The time of day, day of the week, season and holidays all influence actual travel speeds. Why are our ETAs better? Tapering method ETAs are often unreliable because by the time the vehicle arrives at the spot of an identified traffic jam or incident, the traffic is often flowing again. Our tapering method combines the instantaneous method for ETAs close by with the trajectory method for ETAs further ahead.
  25. 25. All scheduled deliveries, including expected delay in planned arrival. Store display Next delivery based on latest ETA calculation Store location Planned route and actual position transport
  26. 26. Remember two things: 1. Less turnaround time because the unloading team knows when the truck arrives. 2. Less time in traffic jams because the routing and planning system understands daily traffic routines.
  27. 27. Half of our late deliveries have been resolved. This creates savings, but more importantly happy customers
  28. 28. own fleet carriers
  29. 29. Remember one thing: 1. A branded track and trace portal increases the customer experience and allows you to choose the best carrier for the occasion.
  30. 30. Step by step improving our supply chain to improve the information to the store managers having greater insight into the delivery and having increased the delivery performance by 50%
  31. 31. 1. Inform about subsequent delays 2. Change order sequence 3. Swap orders between assets Planning the home delivery
  32. 32. Remember two things: 1. Groceries delivery may start from a shop location or a hub location. 2. When multiple vehicles operate in a small area the planning may be easily adjusted to accommodate for exceptions.
  33. 33. PRE-TRIP ON-TRIP POST-TRIP Impact Continuous learning planning algorithms Continuous optimisation of order sequence within a trip Reallocation of orders between trips Continuous reallocation of assets Continuous planning: No more cut-off moment Complexity Steps to full continuous planning
  34. 34. PRE-TRIP ON-TRIP POST-TRIP Impact Continuous learning planning algorithms Continuous optimisation of order sequence within a trip Reallocation of orders between trips Continuous reallocation of assets Continuous planning: No more cut-off moment Complexity Steps to full continuous planning Reinforcement learning We are using reinforcement learning to create a model that suggests intertrip mutations based on vehicle locations, trip information and ETA updates.
  35. 35. Our vision and trajectory
  36. 36. Thank you

The complete logistics process from planning to delivery consists of many small decisions and many of those decisions are still made by humans. This session presents how better predicted data and automation with the Simacan platform allows shippers and carriers to be more efficient. Speaker: Silvie Spreeuwenberg, Business Controller at Simacan. *Intelligent Mobility 2021: Virtual Conference.

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