DATA, ANALYTICS & AI USE CASE

Transport Optimization

Rethink your transport optimization with new approaches. Boost analyses and simulations with consolidated data from various systems, enabling integrated planning of transport processes and routes.

AI and machine learning in data strategy

Today’s requirements, which often aim for just-in-time deliveries, complicate transport planning and result in underutilized transports. This is usually a trade-off between customer satisfaction and optimal utilization of the existing network. It is important to strike a good balance in this trade-off and increase efficiency to operate more sustainably while keeping costs under control.

The difficulties frequently reside in the subsequent components:

  • In the inbound and outbound network, there is a lack of optimal load bundling and, at the same time, more consolidated processing through hubs
  • Inaccurate, incomplete, or outdated data for optimization
  • Patterns in the route taken by the truck driver
  • TMS (Transportation Management System) that does not consider soft factors, anomalies, or peculiarities in the tours
  • Lack of an external perspective that can identify blind spots.

In transport optimization, questions like: How can I optimally utilize my fleet at different locations to be resource-efficient while covering seasonal variations? What is the relationship between transport costs and higher delivery frequency, considering warehouse costs? How are shipments best planned, and how can shipments be most efficiently assembled? How does a supplier adjustment affect transports and the network? How can I most sensibly combine road, sea, air, and rail traffic? How can I most efficiently load my vehicles? are commonly addressed.

By consolidating data from various relevant systems, analyses and simulations can be facilitated, enabling integrated planning of transport processes and routes.

  • Tactical and strategic analysis of proposed routes from the TMS – Obtain an additional perspective based on additional factors (seasonal variations, misdeliveries, regular detours, etc.).
  • Simulations to visualize and compare changes in various parameters (route modifications, delivery frequencies, volume, fleet, etc.).
  • More efficient representation of shipment volumes through a realignment of transport flows (feeder and main routes).
  • Optimal design of alternative modes of transportation (multimodal planning).
  • Benchmarking the costs of scenarios by comparing them to the current situation.

Optimizing the existing route situation contributes to increasing the efficiency of warehouse and transportation resource utilization while simultaneously leading to the optimized use of all valuable resources. An even distribution of orders across the entire network helps balance peaks and, at the same time, maintains high quality under significant loads. Additionally, network optimization reduces both freight transport costs and expenses for inventory management in distribution and procurement areas. Sustainable optimization leads to a reduction in safety stock, freeing up tied-up capital.

Optimizing transportation provides another central benefit – delivery routes, as well as delivery accuracy, are improved. With transparency across the process steps of the entire network, knowledge from the entire process can be taken into account and utilized to fulfill the promised service quality.

Contact us

Sandro Breandle, Chief data and analytics officer in Log-hub AG

Chief Data and Analytics Officer

Sandro Brändle

Have a question about our Data, Analytics & AI Consulting Services? You can contact our CDAO directly.

Schwandweg 5, Schindellegi

Switzerland 8834

Tel: +381 60 358 52 83

support@log-hub.com

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