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Supercharging A Warehouse’s Performance With AI-Driven Computer Vision

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Supercharging A Warehouse’s Performance With Ai Driven Computer Vision

Computer Vision can be used to improve process adherence in the warehouse

Flymingo is an Israeli computer vision company that identifies supply chain mistakes in operating processes in existing warehouse camera feeds. This is the only supply chain software company that the industry analyst firm ARC Advisory Group knows where AI Vision is core to the solution. They are using a form of AI for image recognition in conjunction with a warehouse management system. The solution is used to drive high adherence to standard operating procedures in a warehouse. SOPs are critical for safety and reliability.

Reinforcement Learning Applied to Computer Vision

Reinforcement learning is a form of machine learning that lets AI models refine their decision-making process based on positive, neutral, and negative feedback. For example, if you want to train a computer vision system to recognize a dog’s image, you will start by using humans to look at tens of thousands of images of animals. The humans label the pictures as dog, not dog, or unclear. The computer is then presented with those images. The system would say, “this is a dog” or this is “not a dog.” The dog recognition algorithm then receives feedback, either positive (yes, you are right, this is a dog), negative (no, this was not a dog), or neutral (we don’t really know if this is a dog or not). As the system goes through more and more feedback loops, its ability to correctly identify an image is honed.

In Flymingo’s solution, security cameras in the warehouse bring images of objects – pallets, trucks, cases, people, staging areas, and so forth – to the system. The system then identifies what type of object is being displayed. The combination of the image, data from the WMS, and contextual rules, then allows the system to understand whether processes are being followed.

How Do WMS and Computer Vision Work Together?

A warehouse management system is a category of application software that supports receiving, put-away, picking, shipping, and value added services. WMS solutions rely on automatic identification systems, often RF scanning devices that read barcodes, to record real-time status changes.

A WMS can drive almost perfect picking and inventory accuracy if SOPs are followed. A warehouse is set up with barcode labels on every slot in the distribution center. Barcode labels can also be placed on cases, pallets, and individual stock keeping units. For example, the WMS tells a floor associate to go to location AX32 and pick two cases. The associate goes to the location, scans the slot barcode label to confirm that he is in the right location, and scans the barcode labels on two cases, which proves that the right number of cases have been picked. Using a WMS in conjunction with AutoID can drive inventory accuracy of over 99.9%.

While a WMS can drive very high accuracy, much higher than any paper-based system, achieving those levels of accuracy depends upon workers not getting distracted and following the SOPs. For example, if a worker goes to the correct slot, and a coworker walks by and starts a quick conversation, the associate might inadvertently pick cases from the wrong slot when he turns back to the rack. Or an associate might be instructed to put a shipping label on a pallet when it is staged in a designated location near the dock. The associate may click a button on his RF device to start the label printer. She is supposed to go get the label, return to the pallet, and put the label on the pallet. However, if she decides to take a restroom break before going to the label machine, a different associate might take her label.

Flymingo’s computer vision system, in conjunction with the WMS, can identify that an error has likely occurred. In the example of putting a shipper label on a staged pallet, the vision system might be set up to flag a potential error if the associate does not return to the pallet location within three minutes. The associate’s manager can be alerted of a particular type of mistake, look at the footage, and determine whether a mistake has, in fact, been made. The manager can then rapidly engage in visual-based coaching. Near real-time feedback enhances workforce performance.

Not all errors result from mistakes. Some associates intentionally violate SOPs. For example, if a warehouse has performance targets, an associate may be told to go to a particular location and pick two cases. They might choose to make up time by not going to the location. Instead, they use their mobile device to indicate that there is no inventory in the slot. In short, they incorrectly assert that the pick could not be made. Or a thief on the shipping dock might intentionally put a pallet in an associate’s truck. In these cases, visual intelligence can be used to detect untrustworthy employees.

Cool Technology, But Does it Work?

Avi Boas, the chief of operations at Abaline, spoke at the Made4net user conference, Inspire 2024. Abaline is a private family-owned distributor with 25 trucks. Abaline’s primary distribution center is a 165,000-square-foot facility located in Bayonne, NJ. The distributor also serves healthcare facilities, educational institutions, and other sectors.

Abaline uses the Made4net WMS solution. Implementing Flymingo on top of the WMS allowed them to significantly improve process adherence. Mr. Boas said that before Flymingo was implemented, they might not have learned they had shipped the wrong medical supplies to an overseas customer for weeks. At that point, he and the shift manager might spend hours looking at stored security footage to determine what went wrong. When the manager talked to the associate about their mistake, the associate would frequently be defensive and blame the mistake on something beyond their control.

Now, the mistake is determined in near real-time. When the employee is approached, that associate can often figure out the mistake they made before the manager even broaches the subject. In addition to improved employee relations, better process adherence has improved customer service.

The post Supercharging A Warehouse’s Performance With AI-Driven Computer Vision appeared first on Logistics Viewpoints.

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The Freight Forwarder Moat Is Getting Shallower

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The Freight Forwarder Moat Is Getting Shallower

Ocean freight forwarding is an $80+ billion market bogged down by the manual processes related to booking management, documentation services, and the coordination labor that holds it all together.

When working with a freight forwarder, you’re buying three things bundled together:

Carrier relationships — access to capacity, negotiated rates, allocation commitments.
Operational data — knowing which carrier fits a given lane, what documents a particular trade corridor requires, how to handle an exception when a booking gets rejected.
Coordination labor — the booking itself, the documents per container (industry estimates range from 9 to 18 depending on the corridor), the re-keying of data across disconnected systems, the email chains chasing confirmations and clearances.

Shippers have always paid for the bundle because you couldn’t get one piece without the others, but that’s changing.

Where the bundle comes apart

Travel agents used to bundle airline relationships, destination expertise, and the labor of putting trips together into a single fee. Aggregator platforms unbundled the pieces, and the booking layer went first because that’s where the volume was. Ocean freight forwarding is in the same position. More than digitizing booking, though, AI is automating it.

The bulk of the volume and labor cost for freight forwarders is tied up in rate comparisons across dozens of carriers, document preparation and routing by trade lane and commodity classification, booking execution against pre-negotiated contracts, and exception triage on rejected bookings.

But this is all high-volume, rule-governed, multi-system coordination where speed and consistency matter more than creativity. Exactly the type of work that AI agents are well-equipped to handle.

Platforms can now ingest a rate agreement, parse surcharges and FAK provisions into a digital rate profile, compare carriers on cost, transit time, and schedule reliability, and execute a booking based on pre-defined parameters, without a human in the loop.

Automating the entire order lifecycle

Every dollar of margin exposure in ocean freight traces back to a decision made without complete information. That means that every action must be rooted in live network data across shipment flows, carrier performance, and insight from inventory and order systems. A platform with that intelligence can automate and accelerate the full workflow from detecting a supply shortfall, selecting a carrier, booking the container, managing the documents, tracking the shipment, and handling exceptions.

A shipper stitching together a rate tool from one vendor, a booking portal from another, a document system from a third, and a visibility feed from a fourth gets digitization. They get a slightly faster version of the same manual process. The full picture still lives in a person’s head, and the handoffs between systems still require human coordination.

While freight forwarders and other intermediaries are also investing in AI, they’re primarily automating their own coordination labor before someone else absorbs it. But they can’t replicate the data advantage of a platform that sits across the entire supply chain.

A forwarder automating its booking desk draws on its own transaction history. A point solution built specifically for ocean booking draws on booking data. A platform processing millions of supply chain events daily across orders, inventory, carrier performance, and live shipment status, has a different signal base entirely. Carrier selection informed by real-time schedule reliability, live network disruption, and your actual inventory positions is structurally more accurate than carrier selection informed by historical rate tables.

The shrinking intermediary layer

The moats around freight forwarders’ profit margins are eroding, and the lines between legacy endpoint solutions are blurring. High-complexity corridors and specialized commodities still need human expertise, but the bread-and-butter containerized freight that makes up the bulk of forwarder revenue is the volume where automated workflows shine.

Meanwhile, software providers will have a hard time selling dashboards and chatbots to specific teams compared to AI-native platforms offering a single operating system across all supply chain operations, and serving downstream stakeholders.

The question for forwarders is how long they can keep patching automation onto a fragmented architecture with a booking tool here, a document system there, people bridging the handoffs in between. And how much revenue sits in structured, repeatable work that a connected platform absorbs?

For shippers, the choice is whether to invest in a platform that automates the order-to-delivery and exception lifecycle, or keep paying others to hold the pieces together. The second option is a decision to fund the intermediary layer sitting between them and their own data.

The post The Freight Forwarder Moat Is Getting Shallower appeared first on Logistics Viewpoints.

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Supply Chain and Logistics News Week of May 7th 2026

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Supply Chain And Logistics News Week Of May 7th 2026

The logistics and supply chain landscape is undergoing a fundamental transformation as industries move from rigid, low-cost models toward strategies defined by agility and resilience. This week’s roundup explores how major players are navigating this shift, from Amazon’s bold move to offer its massive infrastructure as a standalone service to Ford’s strategic manufacturing reset in the EV sector. We also dive into the critical human element in modern cost engineering, the logistical reimagining of energy corridors due to geopolitical risks, and the new AI-driven tools closing the gap between inventory detection and real-time execution. Together, these developments highlight a common theme: the pursuit of flexibility and data-driven intelligence in an increasingly unpredictable global market.

Top Supply Chain Stories from this Week:

Modern Cost Engineering Evolution: Rewiring the Human Element for Supply Chain Resilience

In the latest shift for cost engineering, the focus is moving beyond purely digital tools to address the critical human element required for true supply chain resilience. As industrial organizations transition from traditional backward-looking estimates to modern “should-cost” methods powered by AI and digital twins, the real challenge lies in workforce transformation. Success in this new landscape requires a significant cultural shift, moving away from isolated departmental silos toward cross-functional collaboration. By reskilling traditional estimators to act as strategic consultants—capable of interpreting material science and operational constraints—companies can evolve from simple price negotiation to collaborative manufacturing improvements that ensure mutual profitability and long-term stability.

Hormuz Risk Is Redrawing the Supply Chain Geography of Energy

Geopolitical instability in the Strait of Hormuz is forcing a fundamental shift in energy logistics, moving the industry away from lowest-cost network design toward a risk-adjusted model. With the waterway handling roughly 20% of the world’s oil and liquefied natural gas, repeated disruptions have transformed infrastructure like pipelines, storage terminals, and deep-water ports outside the Persian Gulf into high-value strategic assets. Nations and corporations are no longer viewing these as simple logistics nodes, but as essential escape routes that provide the optionality and recovery time needed to withstand chokepoint failures. This selective redesign of the global energy map signals a new era where geography and physical redundancy are the primary drivers of supply chain resilience.

Ford’s Manufacturing Reset Shows How Automakers Are Rebuilding the EV Supply Chain

Ford’s manufacturing pivot represents a fundamental shift from aggressive electric vehicle expansion toward capital discipline and supply chain flexibility. By taking a $19.5 billion write-down and restructuring battery joint ventures, the company is moving away from rigid, single-purpose production lines in favor of multi-energy platforms that can adapt to fluctuating demand for hybrids and EVs. A key component of this reset is the repurposing of battery manufacturing assets in Kentucky and Michigan for stationary energy storage and data center support. This strategy transforms these facilities into flexible energy infrastructure rather than just automotive supply nodes. Ultimately, Ford is signaling that the next phase of the market will be defined by the ability to manage uncertainty through cross-functional asset utilization and a focus on manufacturing-driven affordability.

How FourKites Connects Stockout Detection to Freight Execution in Minutes

FourKites has launched a unified solution that bridges the gap between stockout detection and freight execution, reducing resolution time from hours to less than five minutes. By integrating its Inventory Twin and Booking Connect AI, the platform eliminates the traditional “manual scavenger hunt” where planners had to jump between ERPs and carrier portals to resolve inventory gaps. The system uses decision intelligence to identify stockout risks up to six weeks in advance and provides ranked recommendations for corrective transfers based on cost, speed, and carrier performance. This closed-loop workflow allows planners to execute optimized shipping options with a single click, addressing the massive financial impact of inventory distortion and reducing the need for expensive, unplanned expedited shipping.

Amazon Launches “Supply Chain Services” Leveraging its Global Logistics Network

Amazon has officially launched Amazon Supply Chain Services (ASCS), a move that decouples its massive logistics infrastructure from its retail marketplace to serve as a standalone utility for all businesses. Similar to the trajectory of Amazon Web Services (AWS), the platform opens up Amazon’s multimodal freight, automated warehousing, and last-mile parcel delivery networks to companies regardless of whether they sell on Amazon. Major early adopters like Procter & Gamble, 3M, and Lands’ End are already leveraging the service to move everything from raw materials to finished products. By consolidating fragmented logistics contracts into a single automated interface, Amazon aims to use its scale—currently moving 13 billion items annually—to provide businesses with end-to-end visibility and 96.4% on-time delivery rates, signaling a significant new challenge to traditional 3PLs and carriers like FedEx and UPS.

Song of the week:

The post Supply Chain and Logistics News Week of May 7th 2026 appeared first on Logistics Viewpoints.

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How FourKites Connects Stockout Detection to Freight Execution in Minutes

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How Fourkites Connects Stockout Detection To Freight Execution In Minutes

FourKites is bridging the gap between identifying a problem and solving it. With the integration of Inventory Twin and Booking Connect AI. Traditionally, supply chain planners have been stuck in a manual scavenger hunt whenever a stockout alert surfaced, jumping between ERPs to find surplus stock and carrier portals to secure freight. This fragmented process typically took hours, often forcing companies to rely on expensive, last-minute expedited shipping or facing steep On-Time In-Full (OTIF) penalties to avoid customer dissatisfaction. By unifying these disparate data streams, the new solution allows teams to detect risks two to six weeks in advance and execute corrective transfers from a single, seamless workflow.

The impact on operational efficiency is significant, reducing the resolution time from detection to execution from several hours to less than five minutes. Instead of just receiving a warning, planners are presented with recommendations powered by Decision Intelligence that include the fastest, cheapest, and most optimal shipping options based on real-time carrier performance data. This closed-loop system directly addresses the 1.73 trillion dollar global issue of inventory distortion and aims to eliminate the 15-25 hours planners previously spent on manual coordination.

By keeping a human in the loop to select the best recommendation with a single click, FourKites ensures that exceptions are resolved without ever leaving the platform. This integration helps protect freight budgets, where unplanned expedited shipping often consumes up to 48% of total spend. This launch represents a shift from reactive firefighting to proactive execution, allowing teams to move away from costly safety stock and focus on high-value responsibilities. Supply chain planner responsibilities are changing with the continued developments of AI and the de-siloing of disparate systems.

FourKites is a supply chain technology provider that operates a global real-time visibility network tracking over 3.2 million shipments daily across 200 countries and territories. By integrating data from 1.1 million carriers across all modes (road, rail, ocean, and air), the platform uses AI-powered “digital workers” to automate exception resolution and provide predictive insights. More than 1,600 global brands, including leaders in the CPG and Food & Beverage sectors, trust FourKites to transform their logistics from reactive tracking into proactive, intelligent orchestration.

Read the full ARC brief breaking down the new FourKites solution here: https://www.fourkites.com/research/arc-advisory-stockout-detection-freight-execution/

The post How FourKites Connects Stockout Detection to Freight Execution in Minutes appeared first on Logistics Viewpoints.

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