Enmovil’s load and route optimisation algorithms streamline operations, reduce dispatch cost and increase productivity

A leading automotive OEM ships nearly $300M worth automotive spares nationwide to their dealer network using the transporters' fleet. On a daily basis, the shipment is done to hundreds of dealers. The pre-loading planning and load allocation alone consumes around 25-30 man-hours daily. There is also an absence of a metric to evaluate the cost-effectiveness of the allocation.


The shipments have to satisfy many constraints:

  • varied capacity and cost per unit volume of trucks,

  • minimum threshold of permitted material,

  • maximum wait time for delivery,

  • transporter serviced routes and stops and many more.

The challenge for the OEM was to allocate material to the transporters such that he can

  • maximize the load carried by each vehicle,

  • honor the transporter share-ofbusiness and

  • reduce his logistics cost.

Additionally, aim to reduce the number of man-hours dedicated daily towards trying to optimally plan the allocation of material to the transporter before the loading.


The customer has been able to reduce his dispatch cost by more than 8% by leveraging our learning driven planning & optimization algorithms.

The customer has also recovered thousands of man-hours that have been deployed for more productive activities.

The solution can further streamline the logistics operations and bring complete transparency in dealer consumptions. It enables focused drive to improve sales and bottom-line.


Designed specifically for manufacturers to maximize the vehicle utilization and minimize the total cost of shipping. Some key features of this module are:

  • Instantaneous load allocation : Leverage advanced algorithms for an automated instant load assignments experience and save thousands of man-hours in planning.

  • Configurable priorities : User configurable cost optimization parameters like $/KM, $/CMT and many more.

  • High performance multi-threaded optimization : Multiple heuristics based optimal solution extraction using multi-threaded algorithms for parallel execution.

  • AI driven dealer allocation : Autometrics analyses vehicle usage pattern and deployment priorities of various transporters. It performs new allocations based on this intelligence thereby enabling recommendations to be more aligned with transporter and dealer preferences.

  • Intelligent load sequencing : Enable loading of material onto a truck based on target unloading sequence of material. Significantly reduce material unloading time at the dealer with loading sequence recommendations.