Overcoming the Logistics Crisis with Tech: Advanced Cases of Labor Saving by AI and Robotics

This article shares advanced examples of major companies successfully implementing labor-saving measures (optimization of personnel systems) in the logistics industry through the utilization of AI and robotics.

The driver labor time regulation, known as the 2024 problem, has caused an unprecedented labor shortage in logistics, demanding fundamental solutions that go beyond conventional measures.

Transforming Warehouse Operations: Robotics for Productivity Gains

The introduction of robot technology is accelerating in warehouse operations, which were traditionally labor-intensive.

Daikin Industries: 15% Productivity Boost with AGV

Daikin, a major air conditioning company, automated the conveyance of heavy parts by introducing AGVs (Automated Guided Vehicles) in their factories and logistics centers. This resulted in:

• Reduced personnel costs from unmanned conveyance
• A 15% increase in overall process productivity

By substituting simple tasks with robots, human resources are concentrated on more value-added work—a clear model for labor saving.

Nippon Express (Nittsu): Optimizing Picking Routes with AMR

Next, we look at the efforts of Nippon Express (Nittsu).

The AMRs, driven by advanced AI autonomous control, instantly calculate the optimal picking route in the warehouse and guide the workers.

DX in Delivery: Big Data and AI Analysis as a Source of Competitiveness

DX (Digital Transformation) is also advancing in the delivery sector, where big data and AI analysis are sources of competitive advantage.

Yamato Transport and Others: AI for Optimal Vehicle Allocation

Companies like Yamato Transport and global logistics firms like UPS are standardizing AI-driven vehicle scheduling.

The latest systems analyze vast amounts of data (past delivery records, real-time traffic, weather, volume) to determine optimal delivery routes.

This has achieved a reduction in vehicle mileage by over 5%, benefiting both cost reduction and $\text{CO}_2$ emission reduction.

Resolving the Last-Mile Issue: A 90% Reduction in Missed Deliveries

Field trials are underway to solve the last-mile challenge of missed deliveries.

A University of Tokyo project validated AI-optimized delivery scheduling.

The method uses life logs (e.g., electricity usage data), while respecting privacy, to predict the probability of being home, proposing the best delivery time.

The trial achieved a temporary, yet remarkable, data point of an approximate 90% reduction in missed delivery rates.

Expectations are high for the practical application of AI prediction technology to resolve the redelivery problem.

The examples from Daikin, Nittsu, and Yamato indicate that logistics is transitioning from a labor-intensive industry to a technology-driven capital-intensive industry.

Accelerated investment in AI and robotics, driven by the labor shortage crisis, is building a highly productive and sustainable logistics network.

DX will be a crucial factor for corporate competitiveness, not just transport capacity.

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