3 Ways Algorithms Enable Logistics and Distribution Optimisation

Every step of retail supply chain management so far — demand forecasting, workforce optimisation, and pick & pack — has led to this point: Ensuring orders are delivered on time in full (OTIF) with streamlined logistics and distribution. Accuracy is a strategic priority for retailers as even small delays and errors can result in operational turmoil, customer dissatisfaction, and revenue loss.

For these reasons, large retailers use their scale and negotiation power to share the pressure with manufacturers and logistics providers, imposing stiff penalties for missing promised delivery windows. Supply chain partners are, therefore, left with no choice but to find ways to cope with imposed industry norms.

And the task is complex. Multiple factors influence the transport of goods between two or more locations, and they are often beyond anyone’s direct control. So how can shippers overcome the challenge and meet delivery SLAs while, at the same time, maximising asset efficiency?

Intelligent algorithms give a fresh perspective to supply chain players, allowing them to build on established rules to, run predictive analytics across scenarios, and facilitate decision-making in real time. Let’s take a look at some applications that enable logistics and distribution optimisation.


Low margins, capacity constraints, and retailers’ expectations have transformed the way loading is done. Logistics providers now need to optimise capacity utilisation while keeping some flexibility in order to deal with contingencies — e.g., last-minute orders, rerouting, and cancellations.

Algorithms are well suited to make sense of such dynamic and unpredictable realities. They provide resilience to cope with the inevitable changes taking place and can identify the most contextually adequate clustering solution — i.e., how to group orders best — bearing in mind stackability, destinations, and delivery windows.


Getting loads ready is only one part of logistics and distribution optimisation. Shippers must also decide how goods will be transported to maximise the number of utilised hours per truck and minimise empty mileage. And this requires considering multiple predefined parameters:

  • Available staffing resource
  • Drivers’ requirements and home base
  • Regulations regarding carbon emissions, driving hours, and rest periods
  • Expected congestion at various times throughout the day or week
  • Road conditions and suitability for different vehicle classes

Algorithms ensure drivers can complete the delivery of as many loads as possible while complying with the law. As new conditions emerge, such algorithms can also automatically recalculate estimated times of arrival (ETA) and warn retailers about changes potentially affecting agreed schedules.


Algorithms can also make recommendations about how to streamline pick and pack. For example, let’s say that a new truck with a different loading capacity is allocated for a specific delivery. New set of rules can be incorporated such that batching and 3D cubing is optimised accordingly to fill this truck most efficiently.

Additionally, drivers and retailers can flag damages that occurred during transportation such that manufacturers and shippers’ algorithm powered systems can prevent these in the future — e.g., by selecting packaging material and size better or avoiding putting certain items together in the same crate.


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