Limited releases and strategic drops, blind-box surprises, and a well-crafted global distribution – these are just a few reasons powering cult-fave Labubu’s meteoric rise.
This little Nordic myth-inspired toy was the brainchild of Hong Kong-born artist Kasing Lung, who created a fairytale universe called ‘The Monsters’ in a trilogy of picture books. In 2019, Lung partnered with Beijing-based Pop Mart, a toy retailer known for its blind-box collectibles. With a mix of engineered drops fueled by social media hype, Labubus today have brought home at least 350% in plushie profit in H1 2025, reports say.
What do these changing dynamics and new retail models augur for the supply chains behind them? One could argue we’re seeing the bullwhip effect in play, where small shifts in point‐of‐sale demand lead to increasingly larger fluctuations in orders and inventory levels as one moves up the supply chain. And, here’s just one window of the many opportunities that AI can help supply chain leaders tap into.
How can AI build resilience, provide real-time intelligence, aid demand planning, compliance and supplier relations for future-proofed supply chains?
In this issue of AI of the Tiger, we delve into tried-and-tested real-world case studies and success stories on how AI-powered supply chains are rewriting the game. From optimization for efficiency and resilience across changing supplier, market and consumer dynamics, to using GenAI, Agentic AI, LLMs, and Synthetic data to power visibility through real-time tracking and data analytics, and agility to manage risk and adapt quickly – Step into a brave new Labubu-verse.
Data without borders: Linking warehouses, fields, and forecasts
$17M in annual savings, 4% lower shipping costs, 75% less manual effort on audits – that’s the impact of a unified data architecture built for visibility and scale. From warehouse ops to crop yield, we partnered with a leading F&B brand to engineer a connected supply chain powered by intelligence at every layer. What did this look like? Near real-time dashboards for multi-site warehouse ops, ML models predicting crop growth using satellite imagery, early risk detection for delayed orders with dynamic routing recommendations, and automated planogram compliance through a mobile app with a reusable data model, automated DevOps pipelines, and a self-service portal for streamlined governance. Learn more.
Intelligence in-stock: When inventory thinks for itself
What does it take to turn a continuous function like inventory optimization into a smarter one? We collaborated with a global tools and hardware manufacturer to transform it from a cyclical process to a capability that adapts in real time. By embedding dynamic safety stock planning, automated shortage detection, and SKU-level rebalancing into a unified platform, we brought together BOMs, vendor inputs, and demand signals into a single decision layer. With real-time spend visibility and a diagnostic OTIF framework in place, the initiative delivered $18.1 million in savings by optimizing working capital and $20 million through improved inventory management across plants. Read more.
From what-ifs to what-nexts: Stress-testing hypotheticals at scale
When modeling lead-time disruptions at scale, even a 0.5% variance in demand forecasts can cascade into millions in excess inventory or stockouts. That’s why we developed a controlled, synthetic-data-driven test bed, letting teams accurately run disruptive multi-variable scenarios without affecting production systems. As part of our collaboration with a leader in athletic apparel, footwear and equipment, we built a simulation platform to model 80+ operational scenarios across 10 million orders, supporting proactive risk planning and more precise policy decisions. This approach delivered +98 pp improvement in simulation accuracy for Stock Transfer Order (STO) breadth and +36 pp for air freight forecasting. Learn more.
Supply chain disruptions are more blind-box drops that rarely come with a polite heads-up. The ones that thrive are those that practice adapting, in safe sandboxes and measured simulations. They’re engineered to absorb the knocks and bounce back with that cheeky Labubu grin. How do you use tech to turn a curveball into an advantage before the inning’s over?
This edition was originally published on LinkedIn on August 12, 2025.
