Order Profiling for Warehouse Layout and Picking Optimization

Analyzed warehouse order patterns to identify SKU activity, demand trends, and picking behaviors, leading to recommendations for warehouse layout and fulfillment optimization.
Warehouse Optimization
Logistics
Supply Chain
Data Analytics
Author

Luciana Claure Parada

Published

October 20, 2025

Project Overview

This project analyzed warehouse order and SKU data to identify the operational factors driving most picking activity. By profiling item demand, order composition, and handling patterns, I developed recommendations to improve warehouse layout, reduce travel time, and increase fulfillment efficiency.

The analysis showed that a relatively small percentage of SKUs generated most warehouse activity, providing opportunities to redesign storage locations and streamline picking operations.

Tools

  • Microsoft Excel
  • Data Analysis
  • Pareto Analysis
  • Warehouse Profiling
  • Supply Chain Analytics

Objectives

The project focused on answering several operational questions:

  • Which SKUs generate most picking activity?
  • Which products account for most customer demand?
  • How complex are customer orders?
  • Should the warehouse prioritize full-carton or broken-carton picking?
  • Which product families should be stored together?
  • What order quantities occur most frequently?

1. Item Activity Profile

Objective

Identify which SKUs generate the majority of warehouse picking activity.

Key Findings

  • 1,750 SKUs generated 80% of all picking activity
  • These represented only 32.5% of all SKUs

Recommendation

Locate these high-activity SKUs in the most accessible storage locations to reduce travel distance and improve picking efficiency.

2. Demand Profile

Objective

Determine which SKUs account for most customer demand.

Key Findings

  • 675 SKUs generated 80% of total demand
  • Only 12.54% of all SKUs accounted for most product movement.

Recommendation

Assign high-demand products to locations that simplify replenishment and minimize stockout risk.

3. Lines per Order

Objective

Measure order complexity.

Key Findings

  • 57.68% of orders contained only one line
  • 39.07% contained only one item

Recommendation

Create a dedicated fast-picking lane for single-line orders to eliminate unnecessary sorting and reduce fulfillment time.

4. Order Quantity Profile

Objective

Determine whether operations should focus on full-carton or broken-carton picking.

Key Findings

  • 99.16% Broken-carton picks
  • 0.31% Full-carton picks
  • 0.54% Mixed picks

Recommendation

Design the warehouse primarily for broken-case picking while maintaining a small area for occasional full-carton orders.

5. Item Family Profile

Objective

Analyze which product families are commonly ordered together.

Key Findings

  • Shirts represented 90.25% of single-family orders.

Recommendation

Store shirt SKUs together in high-access locations while positioning frequently paired families nearby.

6. Units of Handling

Objective

Identify the most common order quantities.

Key Findings

Most frequent order quantities were:

  • 10 units
  • 5 units
  • 15 units
  • 1 unit

Recommendation

Introduce 5-unit and 10-unit inner packs to reduce counting time and improve picking accuracy.

Overall Recommendations

Based on all warehouse profiles, I recommended:

  • Relocating high-activity SKUs to premium picking locations.
  • Designing the warehouse primarily for broken-case picking.
  • Creating a fast lane for single-line orders.
  • Grouping shirt SKUs strategically.
  • Introducing inner-pack packaging for common order quantities.

Impact

This project demonstrates how warehouse profiling and Pareto analysis can guide facility layout decisions, reduce travel time, simplify picking operations, and improve overall warehouse productivity.

Skills Demonstrated

  • Warehouse Design
  • Facility Layout
  • Pareto Analysis
  • Supply Chain Analytics
  • Logistics Engineering
  • Process Improvement
  • Industrial Engineering