Route Optimization using AI

Efficiently plan and continuously optimize your routes. Achieve a state of operational excellence through continuous monitoring and recalibration. Anticipate and mitigate potential disruptions.

AI and machine learning in data strategy

With the constant changes and usually increasing volume of goods flows, networks become increasingly demanding and complex. Businesses already have mechanisms and systems in place to plan goods flows wisely. However, these mechanisms and systems are often not the most intelligent, typically relying on familiar approaches. There is a lack of simulation and comparison capabilities to assess costs, time, and other factors for efficient consolidation of transports to reduce costs and enhance efficiency.


    • Lack of transparency in the provided data
    • Lack of knowledge about effective transport capacities
    • Established routes and tours over the years that are challenging to break
    • Unaccounted seasonal variations
    • Resource planning
    • Lack of awareness about the impact of departure and arrival times and the potential additional costs
    • Data silos – No shared, consolidated database

    Through analyses and simulations, routes can be optimized and sustainably designed:

    • Delivery channel simulation for various scenarios of a possible new freight tender
    • Conducting Milk Run calculations based on existing shipment data, including potential growth
    • More efficient representation of shipment volumes through a realignment of transport flows (feeder and main routes)
    • Evaluation of consolidation concepts and existing or potentially new transshipment points
    • Delivery frequency simulations to calculate how transport and storage costs develop based on the defined scenarios
    • Determination of transport capacity based on potential volume, taking into account the existing network and transport structure
    • Optimization of transport segmentation into procurement and distribution

    The increased transparency through data and simulations allows for the comparison and optimization of existing routes through AI. This initially leads to the optimization of current structures. Furthermore, transport routes can be gradually improved through the re-planning of internal and external transshipment points. This results in better infrastructure utilization, leading to cost reduction. It enhances the overall network utilization during peaks and contributes to a more even distribution of transport capacities, minimizing special operations or overtime.

    By orchestrating a systematic optimization of routes through continuous monitoring and recalibration, businesses can achieve a state of operational excellence. This involves not only responding to current challenges but also anticipating and mitigating potential disruptions in advance. The dynamic synergy of data analytics and AI in route optimization establishes a foundation for future-proof logistics management, where adaptability and efficiency become synonymous with operational success.

    In conclusion, the integration of data-driven insights and AI-driven optimization in route planning is not a mere technological augmentation; it is a strategic imperative.

    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


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