Warehouse Pick-Path and Slotting Optimisation Tool for Indian Logistics

Objective: To develop a data-driven tool that optimises pick-paths and slotting in warehouses, reducing order fulfillment time and improving operational efficiency.

Problem & Context: Indian logistics companies face significant challenges in warehouse operations due to rapid e-commerce growth, high SKU variety, and labour-intensive processes. Inefficient pick-paths and poor slotting strategies lead to increased fulfillment times, higher labour costs, and frequent errors, hampering competitiveness.

Approach & Methodology: The project involves mapping warehouse layouts, collecting historical pick data, and analysing SKU movement patterns. Using optimisation algorithms and simulation techniques, the team will design a tool that recommends optimal slotting and pick-paths specific to Indian warehouse constraints (space, labour, SKU diversity).

Deliverables & Analysis: Key deliverables include a prototype optimisation tool (Excel/Power BI/Python), analysis reports on time/cost savings, and visualisation dashboards. The tool will be tested on real or simulated Indian warehouse data to demonstrate improvements.

Business Impact & Decision: The solution enables Indian logistics managers to make data-backed decisions on warehouse layout and SKU placement, directly reducing operational costs, improving order accuracy, and supporting scalability in response to market demand.