How to evaluate the performance of Intelligent Selective Lift Storage?

2026-04-06 16:26:21

Evaluating the performance of Intelligent Selective Lift Storage requires comprehensive testing and validation based on three core dimensions: budget control, response speed, and system integration, combined with actual business loads. This is not only a technical selection issue, but also a long-term investment decision for warehouse operation efficiency.

1、 Intelligent Selective Lift Storage Budget Assessment: Match development stages to avoid overinvestment or performance bottlenecks

The budget should not only include procurement costs, but also the full cycle investment for deployment, operation, and expansion.

Initial investment: Small and micro enterprises can choose modular lifting cabinets (50000 to 200000 yuan), while medium and large enterprises need to consider integrated systems (over 200000 yuan)

Hidden costs: installation and debugging costs, training costs, interface development costs (such as WMS docking)

Key points of ROI calculation:

Labor saving: Comparing the picking time between "people looking for goods" and "goods to people"

Space benefits: Rent savings brought by increased storage density per unit area

Cost reduction of errors: after-sales and customer loss losses caused by incorrect or missed shipments

Suggestion: Prioritize systems that support phased expansion to reduce initial financial pressure

2、 Response speed: a core indicator for measuring system efficiency

The response speed directly affects the ability to fulfill orders, especially in high concurrency scenarios.

Key indicator definition:

Task response time: The delay from the system issuing instructions to the elevator starting (ideal value<1 second)

Access cycle time: The total time required to complete a pickup and return (≤ 15 seconds for small systems and ≤ 3 seconds for large systems)

Throughput: The number of tasks that can be processed per unit time (e.g. ≥ 80 times/hour for moderate load)

Testing method:

Load testing: Simulate daily order peaks and observe whether the system responds stably

Stress test: Continuous high-frequency command input to detect whether the system will lag or lose tasks

Random Access Test: Simulate random access to multiple storage locations and evaluate the efficiency of scheduling algorithms

Tip: In actual testing, it is necessary to combine the distribution of real SKUs with the frequency of inbound and outbound shipments to avoid misleading empty load testing