F布行: Warehouse Layout Optimization Data for Multi-Category Textile Inventory

An 18.46% reduction in average picking distance was achieved through a data-driven warehouse optimization study for a textile supplier. The research analyzes 4000 fabric SKUs from F布行 in Zhili Town, using EIQ-ABC and K-Means clustering to design a dynamic storage allocation scheme based on sales heat and seasonal patterns. The dataset, last updated in May 2026 and shared under a CC-BY-4.0 license, includes supporting files in PNG, DOCX, and XLSX formats.

Use Cases

• Clustering SKUs based on seasonal outbound frequency and volume mentioned in the description
• Simulating warehouse picking path optimization using Manhattan distance calculations described in the study
• Designing dynamic storage allocation strategies based on 'hot-sale front placement' and 'seasonal elastic zoning' concepts
• Benchmarking EIQ-ABC analysis methodologies for inventory classification in multi-SKU environments

Strengths

• Analysis is based on 4000 fabric SKUs, providing a substantial item base.
• The described optimization framework achieved an 18.46% reduction in average picking path distance.
• The study explicitly addresses seasonal demand patterns (Spring-Summer vs. Autumn-Winter).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The 2.0 MB size suggests the dataset is small, potentially limiting the scope of analysis.

Provenance

Source

figshare

Collection Method

The data appears to be generated from an academic case study analyzing sales and outbound logistics data from F布行, a textile supplier in Zhili Town.

Time Range

The study analyzes seasonal patterns for Spring-Summer (March-August) and Autumn-Winter (September-February).

Freshness

Last updated 2026-05-10 11:48:38; freshness should be verified.

Geography

The case study focuses on F布行, a business located in Zhili Town, China.