Industrial Logistics & Supply Chain Group
AIoT Solutions for Industrial Logistics & Supply Chain Operations
Portfolio Companies under Industrial Logistics & Supply Chain Group
PlantLog AI
PlantLog AI addresses the movement of materials inside manufacturing plants, where production continuity depends on getting the right components, tools, containers, and work-in-progress to the right workstation at the right time. Its AIoT-based systems can help coordinate line-side replenishment, kitting, milk runs, tugger routes, forklift dispatch, staging, and internal material transfers. AI can analyze production priorities, material locations, vehicle availability, and movement history to recommend more efficient delivery sequences and identify bottlenecks before they disrupt manufacturing.
For large plants, location-based AI can also help operations teams find forklifts, carts, pallets, bins, and personnel without relying on manual calls or radio communication. Computer vision and AI can support identification of materials and movement events around production zones, while connected location data provides operational context. The result is more predictable intralogistics, shorter material-search cycles, better utilization of internal transport resources, and tighter synchronization between manufacturing and material handling.
WareDist AI
WareDist AI focuses on the physical flow of goods through distribution centers and warehouses, from receiving and putaway through picking, staging, loading, and dispatch. AIoT systems can help determine where inventory should be positioned, which items should be retrieved first, and how warehouse personnel and material-handling equipment should move through the facility. AI can examine order profiles, storage locations, picking activity, congestion, and equipment availability to improve wave planning, slotting, replenishment, and fulfillment sequences.
A major application is reducing the time lost looking for pallets, roll cages, forklifts, carts, and individual stock items. Location-aware software can reconstruct where an asset or shipment was last handled and help workers locate it quickly. At shipping docks, AI can support trailer staging and loading coordination by matching outbound loads with their assigned dock positions and handling resources. For high-volume distribution operations, these capabilities can reduce unnecessary travel, improve dock throughput, and increase confidence in inventory and shipment status.
YardOps AI
YardOps AI is designed for the complex physical environment between industrial facilities, terminals, distribution centers, and transportation networks. Its AIoT systems can help coordinate trailers, containers, chassis, yard tractors, trucks, and cargo as they move through gates, staging lanes, parking areas, loading zones, and storage blocks. AI can evaluate current yard positions, appointment priorities, dwell times, equipment availability, and planned movements to recommend where vehicles or containers should be positioned next.
Gate and dispatch operations are another important application. AI can associate vehicle identity, driver authorization, assigned loads, and yard locations to help streamline entry and exit decisions while supporting controlled areas. Yard teams can also use location histories to investigate extended trailer dwell, misplaced containers, unnecessary reshuffling, or delayed pickups. For container terminals and industrial yards, these capabilities can turn a constantly changing physical yard into a more predictable sequence of movements, helping reduce congestion and improve equipment utilization.
ColdChainLog AI
ColdChainLog AI focuses on the movement of temperature-sensitive products through refrigerated warehouses, cold rooms, loading docks, distribution facilities, and transportation operations. AIoT systems can help logistics teams maintain precise knowledge of where pharmaceutical products, food products, biological materials, and other temperature-sensitive shipments are located during receiving, storage, staging, picking, and dispatch. AI can identify delays or unusual handling sequences and prioritize operational actions before a shipment becomes vulnerable to extended exposure or missed delivery windows.
Location intelligence is particularly valuable when multiple pallets, containers, totes, and refrigerated transport units are handled simultaneously. AI can associate product identity with storage position, handling events, shipment assignment, and movement history, supporting stronger chain-of-custody records. Warehouse personnel can quickly locate specific loads instead of manually searching refrigerated zones, while logistics managers can investigate dwell time and movement exceptions. These applications help cold-chain operators coordinate product handling more precisely while supporting traceability requirements for regulated and high-value goods.
MROLog AI
MROLog AI addresses the logistics problems surrounding maintenance, repair, and operations materials, where a missing bearing, valve, motor, seal, tool, or replacement assembly can delay critical maintenance work. Its AIoT-based systems can help maintenance organizations locate spare parts across central stores, satellite stores, maintenance workshops, production areas, and staging locations. AI can correlate maintenance work requirements with part availability and physical location, helping teams determine which components are immediately accessible and which require retrieval or replenishment.
Planned shutdowns and turnarounds create another strong use case. AI can help assemble required parts into maintenance kits, verify that components have reached designated staging areas, and identify shortages before technicians arrive at the job site. Historical consumption and work-order information can also help distinguish fast-moving maintenance items from low-frequency, high-criticality spares. Instead of treating MRO inventory simply as stored stock, the system can connect parts availability with actual maintenance execution, reducing technician waiting time and improving readiness for corrective, preventive, and turnaround activities.
BulkMat AI
BulkMat AI serves operations where large quantities of bulk commodities must move between stockpiles, bins, silos, hoppers, processing areas, truck-loading stations, rail facilities, and other handling points. AIoT systems can help establish where material-handling equipment and transport units are positioned and which resources are available for the next movement. AI can use this operational picture to improve dispatching, loading sequences, material allocation, and movement priorities according to production and shipping requirements.
Mining, aggregates, cement, metals, chemical processing, agriculture, and other bulk-material operations often involve long travel distances and heavy equipment. AI can identify unnecessary equipment travel, prolonged idle periods, repeated repositioning, and inefficient transfer paths. Where distinct batches or material grades must remain separated, AI-based identification and movement records can help maintain material provenance from storage through loading or processing. These applications can improve coordination between stockyard operations, processing facilities, transportation teams, and production schedules while reducing avoidable handling activity.
Packpal AI
Packpal AI focuses on the physical handling of pallets, containers, packaging materials, and finished goods during packaging and palletization operations. AIoT systems can help manufacturers coordinate pallet availability with production schedules, packaging requirements, staging capacity, and outbound shipment priorities. AI can determine which packaging resources are required for a production order and help identify where those resources are located, reducing interruptions caused by missing pallets, containers, wraps, bins, or other packaging materials.
The system can also support finished-goods staging after palletization. AI-based identification can help associate a completed pallet with its production order, destination, storage position, and shipment assignment, while location information helps warehouse teams retrieve the correct unit when required. Reusable pallets and containers can be tracked through production, storage, shipment, return, and reuse cycles, making it easier to identify losses and excess accumulation. For high-throughput packaging operations, these applications can improve pallet flow, reduce staging confusion, and connect packaging activities more closely with warehouse and distribution requirements.
Building the Future of Industrial AI and IoT for Industrial Logistics & Supply Chain Industry
For more than three decades, GAO Group of Companies has invested heavily in R & D of industrial IoT. Since generative AI was proven useful in industrial applications, we have been developing AI and IoT technologies, and we have founded Aperture Venture Studio to build, launch, and scale next-generation AI and IoT companies for various industries.
Aperture has been able to attract top AI and IoT technical experts, entrepreneurial and operational executives, influential investors and industry leaders as corporate partners.
Furthermore, we have developed a successful TekSummit and Aperture Ventures Summit to discuss advanced topics on AI and IoT.
As a result, we have built diversified ecosystems and vibrant technical communities for AI and IoT.
To address the enormous and fast-rising demands of AI and IoT in Industrial Logistics & Supply Chain Industry, we have created Aperture Venture Studio.
Join Aperture Venture Studio
We welcome:
- Advisors, co-founders, founding members and prospective employees
- Strategic, venture, angel, and institutional investors
Let’s work together to build the future of AI and IoT for Industrial Logistics & Supply Chain Industry.