Japan’s logistics industry faces a structural crisis driven by labor shortages, aging infrastructure, and surging e-commerce demand. At the heart of this challenge lies a deep reliance on individual experience—what industry insiders call “属人性” (zokusei sei), or person-dependent knowledge—where veteran drivers’ intuitive understanding of routes, delivery patterns, and customer behavior has long sustained operations. However, as workforce pressures mount and regulatory changes like the 2024 overtime limits take effect, this model is becoming unsustainable. In response, SG Holdings Group (SGH), parent company of Sagawa Express, has accelerated its shift from experience-based logistics to a data-driven model, leveraging AI, cloud computing, and digital transformation to build a more resilient and scalable system.
The transition began around 2018, when SGH recognized that rising parcel volumes—reaching approximately 1.4 billion annually at Sagawa Express, with peak daily volumes of up to 5 million—would outpace available labor. While about 90% of delivery slips had already been digitized through e-commerce growth, the remaining 10%, or roughly 400,000 to 450,000 handwritten slips per day, created a critical bottleneck. These slips, often carbon-copy forms with smudges, varying pen pressure, and physical wear, resisted early AI-Optical Character Recognition (OCR) attempts. After years of refinement, SGH achieved 99.9% accuracy in reading numbers by 2022, and by April of that year, completed full digitization of handwritten addresses. This allowed all parcel data to be available by 4 a.m. Each morning, enabling better planning and reducing drivers’ morning workload.
With data now flowing reliably, SGH introduced tools like the “夜積みアプリ” (Night Loading App), which lets drivers pre-register optimal loading patterns for their delivery zones. The app then guides them on how to stack packages efficiently, cutting loading time by an average of 20–30%, with some branches reporting 15–20 minutes saved per driver. Combined with the “スマート集配” (Smart Collection and Delivery) system—which uses AI to suggest optimal routes based on parcel data, maps, and historical performance—these changes reduced average driving distance by 5–10%. Importantly, they leveled the playing field, allowing newer or subcontracted drivers to perform efficiently without relying on years of accumulated experience.
Yet SGH leadership saw that route optimization alone was insufficient for long-term resilience. As Executive Planning Department Director Kazuki Nanbu noted in 2024, traditional AI routing worked well for stable, scheduled deliveries but struggled with the volatility of residential delivery, where daily volume fluctuations, time-specific requests, absenteeism, and local geography create complex variables. To address this, SGH partnered with Google Cloud Japan (GCJ) in 2024 to enhance its logistics analytics capabilities. By combining on-the-ground data—such as package type, volume, timing, customer preferences, and location—with Google Cloud’s data analysis platform, the company aims to transform route planning from experience-based intuition into a scientific, adaptive process capable of handling real-time variability.
This collaboration is part of SGH’s broader vision to evolve from a transportation provider into a total logistics solutions company. With a fully digital data foundation now in place, the group is pursuing higher-order optimizations: integrating warehouse and delivery data for sharper demand forecasting, linking domestic and international logistics to shorten lead times, and using AI to analyze real-time factors like traffic, weather, and customer behavior to generate dynamic delivery plans. Environmental sustainability is also a growing focus, as data enables visualization of CO₂ emissions across routes and modes, supporting green transformation (GX) goals for both SGH and its corporate clients seeking to measure supply chain impacts.
Internal data sharing across SGH’s subsidiaries remains a challenge, but overcoming it could unlock significant value—such as improved asset utilization and predictive analytics. External experts, including Gartner Japan’s Vice President and Team Manager Tatsuya Isshiki, emphasize that moving from experience-based to data-driven decision-making reduces inconsistency and bias at scale, helping companies avoid costly inefficiencies. “Data is not easy to handle,” Isshiki noted, “but when understood and applied correctly, it enables scientific business practices. We are in the AI era, and AI is, by definition, a machine that behaves scientifically based on data.”
SGH’s journey reflects a broader shift in global logistics: from reliance on tribal knowledge to systems where data and AI augment human judgment. As the company continues to refine its models, the goal is not just efficiency, but adaptability—building a logistics network that can withstand labor constraints, demand swings, and environmental pressures while maintaining service reliability. The next milestones will likely involve deeper AI integration, expanded cloud analytics use, and measurable progress toward full data interoperability across the group.
For updates on SG Holdings’ digital transformation initiatives, readers can refer to the company’s official integrated reports and press releases. How do you see data reshaping industries beyond logistics? Share your thoughts below and spread the conversation.
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