Designed algorithms to decide optimal freight modes for consignments


The client is a leading global pharmaceutical manufacturer that exports a significant volume of its products to the US and European Union. 

They use two modes of freight for exporting products – aircrafts and ships.

They did not have a standard process for freight mode selection and decisions across India were driven by judgement rather than data. The absence of a standardized model had led to inefficiencies in freight mode selection.

The client had been incurring huge logistics costs due to inefficiencies in freight mode selection (air and sea) and wanted to minimize their logistics cost by leveraging data and analytics. 

The need was for a Decision Model which could standardize the process of freight mode selection and eliminate inefficiencies by bringing in data-driven logic to optimally select the freight mode for any consignment.


  1. Analyzed freight and inventory data from the previous year and built a ML-based Decision Model
  2. Evaluated current systems and processes of freight mode selection
  3. Retrospective study was undertaken to assess the opportunities lost


  1. Operational efficiency: Replaced judgement-based decision making with data-driven decision making, thus minimizing the inefficiencies in the freight mode selection

  2. Reduced logistics cost: Decision Model helped the client select the most optimum freight for a consignment, thereby significantly dropping the logistic costs by 6%

  3. Dashboards helped senior management track logistics costs and raise exception alerts across all major dimensions