Case Study on how ShypVisor helped to reduce Return shipments by 90%

How Chennai based 3PL player used Shypvisor to reduce return shipments by 90% for India’s leading health and hygiene consumer goods brand


Return shipments started to increase from July-2018. Every month there was at least 30% increase in return shipments compared to the previous month. By mid of October-2018, 60 shipments were returned out of 1457(4.1%).  With new projects in pipeline without arresting this returns, there will be significant hit in sales margin


As there was a sudden surge in returns in the recent 75 days, shipments dispatched since the beginning of 2018 were taken for analysis. Shipment details like dispatch date&time, PO Expiry Date, Consignee Location, third party courier players and, vehicle routes were analyzed using inbuilt reports.  Following key insights were derived:

  1. Shipments dispatch were not done as per priority
  2. Incorrect contact numbers of consignee e.g., Store Manager numbers or staffs, who had resigned, were available in SAP under Contact Details.
  3. Pincode and destination location mismatch
  4. Delay in approval of additional charges
  5. Certain Franchises of a courier player were marking RTO without attempting for delivery


Return % based on Category


Based on the insights key actions were taken from last week of October-2018

  1. Based on PO Expiry date shipment dispatch were prioritized.
  2. Updation in the SAP for contact details was taking time. So, leveraged address book repository in Shypvisor and correct contact details of the stores were updated
  3. Integration with Google geo-location APIs available in ShypVisor helped to updated correct locality details based on pincode
  4. In Expense Management, auto-approval was set for critical shipments and for additional charges less than Rs.500 based on distance from hub to consignee location
  5. Analyzed past 12 months of data to find out courier players who are doing better deliveries and shipment planning engine was updated


Return shipment started to decrease and within 30 days there was a drop by 63% and returns were brought down to 0.25% of total orders within 60 days. All those reports were closely monitored every 2 weeks and actions were taken. This helped to bring returns further down by 0.1% within 4 months. When new projects in pipeline were started, as data and process were in place, no spike in returns were faced resulting in better margin.

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