Li-Ning – Fulfillment Center Transformation in 1 Month with HaiPick Climb

Project Overview

Li-Ning is one of China's most prominent global sportswear brands, operating across retail, e-commerce, and wholesale channels with a broad product range spanning footwear, apparel, and accessories. As a brand that competes directly with international sportswear giants in both domestic and export markets, Li-Ning's fulfillment operations must deliver the speed, accuracy, and scalability that modern sportswear retail demands — while managing the particular complexity of handling two structurally different product categories, footwear and apparel, within the same warehouse environment.

The deployment of HaiPick Climb at Li-Ning's fulfillment center, published in November 2025, represents one of the fastest documented go-live timelines in the Hai Robotics case study portfolio: one month from installation to full operational status. The results are equally strong — a 3× storage capacity increase, 96% vertical space utilization, 800+ pieces outbound per hour, and 99%+ picking accuracy — delivered across a unified system that handles both footwear and apparel without categorical separation.

Technical Solution

HaiPick Climb for Mixed-Category Sportswear Fulfillment

The HaiPick Climb system, powered by the HaiClimber rack-climbing ACR robot, is the core technology deployed at the Li-Ning fulfillment center. The HaiClimber physically climbs the racking structure to retrieve totes and cases from storage positions at any height, delivering them to operator workstations positioned at the base of the storage grid. This climbing mechanism eliminates the dedicated robot travel aisles that floor-based ACR systems require, enabling the storage grid to be configured at a density that is structurally impossible in conventional warehouse layouts.

For Li-Ning's specific operational context, the system's most distinctive capability is its unified handling of both footwear and apparel. These two categories present different physical storage characteristics: footwear typically arrives in rigid branded shoeboxes with fixed dimensions and moderate weight, while apparel is folded, flexible, and variable in both form factor and weight. In manual warehouse operations, these differences often drive physical separation of storage zones, with different shelving configurations, picking methodologies, and staffing allocations for each category. HaiPick Climb's tote-based storage model abstracts away these physical differences — both footwear (in boxes) and apparel (folded into totes) are stored and retrieved through the same automated system, with the warehouse execution software managing the picking workflow at the workstation level regardless of category.

Key Features

1-Month Installation to Go-Live The deployment timeline is the most commercially differentiated outcome of this case study. For a brand operating in a competitive sportswear market where fulfillment speed is a direct consumer-facing metric, the ability to transform the warehouse operation in four weeks rather than six to twelve months is a strategic advantage. It minimizes the period of operational disruption, accelerates the realization of ROI, and reduces the project management overhead associated with extended installation programs.

3× Storage Capacity at 96% Vertical Utilization Tripling storage capacity within an existing building footprint through 96% vertical space utilization is the headline density achievement of this deployment. For Li-Ning's fulfillment center, this means the facility can now hold three times the inventory it previously managed in the same building — supporting a broader SKU range, higher safety stock levels, and greater resilience to supply chain variability without the capital cost of a facility expansion or relocation.

800+ Pieces Outbound Per Hour The throughput of 800+ pieces per hour is a system-level outbound fulfillment metric that encompasses both footwear and apparel categories. This figure validates the system's ability to sustain high throughput across a mixed-category inventory profile, where the physical handling characteristics of the two product types differ significantly. The goods-to-person model sustains this throughput by keeping operators stationary at workstations and using the robot fleet to manage all storage and retrieval movements.

99%+ Order Picking Accuracy The 99%+ accuracy standard is consistent with results documented across other HaiPick Climb deployments in the sportswear sector (notably the Anta deployment, which achieved the same accuracy benchmark). System-directed picking at the workstation — where the operator receives explicit instructions for each pick rather than locating and identifying items independently — is the primary mechanism through which this accuracy level is sustained across both footwear and apparel order lines.

Unified Footwear and Apparel Handling The explicit documentation of unified handling for both footwear and apparel within a single system is a meaningful operational detail for sportswear brands evaluating warehouse automation. Many competing solutions require category-specific storage configurations or separate picking zones for boxed footwear versus folded apparel, adding complexity and reducing inventory flexibility. HaiPick Climb's tote-based unified storage model eliminates this categorical separation, simplifying operations and allowing the full inventory to be managed as a single pool.

Results & Benefits

The Li-Ning deployment delivers a combination of speed, density, throughput, and accuracy that addresses the core operational challenges of a growing global sportswear brand's fulfillment operation. The 3× storage capacity increase within the existing building eliminates the immediate pressure for facility expansion, providing a multi-year runway for inventory growth within the current footprint. The 96% vertical space utilization demonstrates that the system has maximized the productive use of the existing building asset — there is no meaningful additional capacity to be extracted from the facility through conventional means.

The 800+ pieces per hour throughput and 99%+ accuracy combination provides the operational foundation for both retail replenishment and direct-to-consumer e-commerce fulfillment at the service levels that Li-Ning's brand positioning requires. The 1-month deployment timeline means that these benefits were realized within a single month of project commencement — a speed of value delivery that is exceptional in the warehouse automation sector.

The unified handling of footwear and apparel within a single system simplifies the operational model significantly, reducing the management overhead associated with running separate storage zones and picking methodologies for different product categories.

Challenges & Solutions

The central operational challenge of the Li-Ning deployment is the combination of multi-category inventory management and space constraint within a growing sportswear fulfillment operation. Managing footwear and apparel together in a single automated system requires a storage and retrieval architecture that is agnostic to the physical differences between the two categories — HaiPick Climb's tote-based model provides this abstraction, treating both categories as storage units regardless of their physical form.

The space constraint challenge is addressed through the vertical utilization strategy at the core of HaiPick Climb's design. The 96% vertical utilization figure is not incidental — it is the result of a deliberate design choice to eliminate the two primary constraints on vertical storage in conventional warehouses (human ergonomics and forklift access) simultaneously, through robot-driven retrieval and replenishment across the full height of the racking structure.

The deployment speed challenge reflects a broader market need in the sportswear sector, where seasonal inventory cycles and competitive pressure on fulfillment speed mean that operations cannot afford extended automation project timelines. The 1-month go-live demonstrates that HaiPick Climb's deployment model is genuinely compatible with the operational rhythms of a fast-moving consumer goods fulfillment environment.