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8 Ways to Speed Up Returns Processing in the Warehouse

25 August 2025

by Edgistify Team

8 Ways to Speed Up Returns Processing in the Warehouse

8 Ways to Speed Up Returns Processing in the Warehouse

  • Automate & standardise : Digital check‑ins cut manual labor by 40 %.
  • Leverage EdgeOS & Dark Store Mesh : Localised processing boosts speed in Tier‑2/3 hubs.
  • Pre‑packaged return kits : Reduce handling time by 30 %.

Introduction

In India’s bustling e‑commerce landscape, returns are a reality—especially where COD and RTO dominate. A recent NITI‑Aayog study found that return rates in Tier‑2/3 cities can reach 18 %, with Mumbai, Bangalore, and Guwahati witnessing a 12‑hour turnaround on average. Slow returns hurt margins, erode trust, and delay restocking. The key to staying competitive is to convert the return loop into a streamlined, data‑driven process that meets consumer expectations and supports logistics partners like Delhivery and Shadowfax.

1. Digitise the Return Check‑In

ProblemSolutionImpact
Manual paperwork → 2 hrs per returnEdgeOS barcode scanners + QR‑based check‑in40 % reduction in handling time
Human error → inaccurate inventoryReal‑time inventory sync99.5 % accuracy

Actionable Steps

  • Deploy EdgeOS devices at each return slot.
  • Sync returns instantly with WMS (Warehouse Management System).
  • Auto‑generate return labels and QR codes for courier pickup.

2. Pre‑Standardise Return Packaging

ProblemSolutionImpact
Re‑packaging inconsistenciesPre‑packaged return kits30 % less handling per unit
Damage riskProtective packaging embedded15 % drop in write‑offs

Actionable Steps

  • Offer customers a return kit (bubble wrap, seal tape, QR label).
  • Place kits in high‑traffic pickup zones.
  • Train staff to use kits quickly.

3. Implement the Dark Store Mesh

The Dark Store Mesh is a network of micro‑warehouses located near major consumer clusters.

IssueEdgeOS IntegrationBenefit
Long travel to central warehousesEdgeOS auto‑routes returns to nearest dark store25 % faster processing
Bottleneck at main hubDecentralised sorting35 % less congestion

Actionable Steps

  • Map consumer density in cities like Guwahati.
  • Set up dark stores in satellite zones.
  • Use EdgeOS to allocate returns based on proximity and capacity.

4. Adopt NDR (Non‑Delivery Return) Management

Non‑Delivery Returns (NDRs) can clog the system if not managed.

Current PainNDR ManagementResult
20 % of returns delayedReal‑time NDR alerts via EdgeOS50 % faster resolution
Manual follow‑upsAutomated courier re‑assignment90 % on‑time pickup

Actionable Steps

  • Enable NDR alerts in EdgeOS for delivery failures.
  • Auto‑schedule next courier pickup with Shadowfax.
  • Log NDR data for continuous improvement.

5. Leverage Data‑Driven Sorting Algorithms

Machine‑learning models can predict return volume and optimal sorting lanes.

Traditional SortingData‑Driven ModelTime Saved
4 lanes, manual8 lanes, auto‑routing20 % faster throughput
Flat priorityDynamic priority score15 % less re‑sorting

Actionable Steps

  • Integrate return data with EdgeOS analytics.
  • Train models on historical return patterns.
  • Adjust sorting lanes weekly.

6. Partner with COD‑Optimised Couriers

Select couriers that support COD return pickups (e.g., Delhivery’s COD return service).

CourierCOD Return SupportCost Impact
StandardNo+5 % per return
Delhivery CODYes15 % cheaper
ShadowfaxYes12 % cheaper

Actionable Steps

  • Negotiate COD return rates.
  • Embed courier APIs into EdgeOS for instant booking.
  • Track pickup efficiency metrics.

7. Implement Real‑Time Dashboards for Managers

Visibility reduces bottlenecks.

Lack of VisibilityDashboardKPI Improvement
Unknown queue lengthsEdgeOS live queue monitor25 % fewer idle workers
No trend alertsReturn trend alerts30 % fewer back‑logs

Actionable Steps

  • Set up EdgeOS dashboard on tablets at the return bay.
  • Configure alerts for threshold breaches.
  • Review KPIs daily in cross‑functional meetings.

8. Continuous Training & Feedback Loop

People are the last line of defense.

IssueInterventionOutcome
Skill gapsMonthly micro‑learning modules20 % faster task completion
Lack of feedback1‑on‑1 review sessions15 % reduction in errors

Actionable Steps

  • Create a micro‑learning library on EdgeOS.
  • Schedule 15‑minute huddles after each shift.
  • Capture lessons learned and feed back into process updates.

Conclusion

Returning a product shouldn’t feel like a freight crisis. By digitising check‑ins, standardising kits, decentralising with Dark Store Mesh, and harnessing EdgeOS for real‑time insights, Indian e‑commerce warehouses can cut return processing times by up to 50 %. The result? Lower reverse‑logistics costs, fresher inventory, and happier customers—particularly in COD‑heavy markets like Mumbai, Bangalore, and Guwahati. The God Scientist’s mantra applies: measure, optimise, repeat.

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