Supply chain disruption cost businesses about $184 billion in 2025, according to the J.S. Held Global Risk Report, and most of that bill still buys faster detection, not faster action.

That figure is usually treated as weather (i.e. storms happen, costs follow.) Treated as a product specification instead, it highlights an operating model that can spot a problem hours or days earlier than it used to, and still cannot move until a person has opened a ticket, convened a call, and re-entered the same data into three systems.

Visibility platforms, control towers, risk scores, digital twins, and exception dashboards have defined the last decade of AI in the supply chain. That decade has been very good at collapsing the time between an event and awareness of it, but it has been far less good at collapsing the time between awareness and a commercial act.

Detection is a ‘solved-enough’ problem

Ask a chief supply chain officer where the AI budget went and the answer tends to follow a familiar list: demand sensing, ETA prediction, supplier risk scoring, inventory optimisation, and lane analytics. These tools work. Forecast error comes down. A vessel delay is flagged before the container misses the cut-off. A second-tier fab outage shows up on a heat map instead of in a customer email.

None of that accounts for the $184 billion. The bill is the interval after the flag: expedite or wait; split the order or accept the miss; retender the lane or pay the spot rate; consolidate two half-empty movements or ship both; swap ocean for air on the SKUs that actually justify the premium. These are bounded, repeatable decisions that sit inside policy, contract, and inventory limits the company already set—and they still queue behind a human inbox.

Surveys keep describing the same lag in different language. A 2026 Knosc survey of mid-market manufacturers and distributors found that supply-chain teams spend 28 percent of their working time responding to disruptions, most of it investigating what happened rather than changing what happens next.

Logistics executives still rank AI as a strategic priority (Capgemini’s 2025 research put an AI-driven “new-gen” supply chain among the top three technology trends for 70 percent of large-company executives) and then report that measurable financial impact remains rare. Gartner found in 2025 that only 23 percent of supply-chain organisations even have a formal AI strategy. The shortfall is not a shortage of models, but a shortage of authority granted to software.

The ticket is the product

Most current deployments are built around the ticket. The model produces a recommendation, the recommendation becomes an alert, the alert becomes a work item, and the work item waits for a planner already occupied with other work items. By the time the planner acts, the option set has narrowed—the alternative carrier’s capacity is gone, the consolidation window has closed, and the supplier’s next production slot is allocated.

That workflow is not a temporary step on the way to autonomy but the product companies bought. Vendors sold insight because insight is easy to demonstrate and easy to govern; action touches money, contracts, service levels, and blame. So the industry automated the part of the job that does not require a signature. FourKites and ABI Research reported in 2025 that only 27 percent of organisations allow AI to take autonomous action, while 52 percent confine it to decision support.

Adding another dashboard to a delayed shipment rarely moves EBITDA as a result. The decision cycle has not changed; it has only been decorated.

Bounded action as the next model

The firms set to take share are not the ones with the tidiest control tower but the ones that pre-authorise a narrow class of moves and let agents execute them while the exception is still cheap.

Retender a lane when the contracted carrier’s ETA slips beyond a threshold and a qualified alternate sits inside the approved rate band. Consolidate outbound waves when fill rates and cut-off times make a combined movement cheaper than two. Swap mode on a defined SKU set when the cost of air is lower than the cost of a missed retail window. Reallocate safety stock across two distribution centres when a forecast miss and a transport constraint line up.

None of that requires a strategy offsite. Each can be written as: if these conditions, then this action, within this spend cap, with this audit trail, and a human only if the case falls outside the fence. That is not a “lights-out” supply chain—it is the same discipline manufacturers already apply to machine control, where the agent may act inside the interlock and escalates outside it. The difference here is commercial rather than physical: the interlock is a policy object – category, supplier tier, mode, dollar limit, and service class – not a PLC.

Three conditions for real change

First, decisions have to be written as policies, not tribal knowledge. If the only place “we will pay air on A-items after 48 hours of ocean slip” lives is in a planner’s head, no agent can execute it. The work of the next two years is less model training than decision design: which moves are reversible, which are capped, and which suppliers and modes are pre-cleared.

Second, execution systems have to accept machine-initiated transactions. An agent that can draft an RFQ but cannot post it is still a detection tool. TMS, WMS, sourcing suites, and carrier APIs need to treat a bounded agent the way they treat a junior buyer with a spend limit—authenticated, logged, and reversible.

Third, accountability has to move with the action. If a retender inside policy goes wrong, the post-mortem should inspect the policy, the data, and the fence, not hunt for the person who “should have checked”. Until that cultural change happens, every agent will be designed to wait, because waiting is how careers survive.

The competitive split

For a while, both models will look alike on a slide—both will have AI, and both will have a control tower. The difference will show up in cycle time from detection to commercial act, and then in service and cost.

Companies that keep buying detection will know about the storm earlier. Companies that authorise bounded action will already have retendered the lane, consolidated the wave, and moved the A-items before the incident call is booked.

Disruption is not going away. Lead times in critical components, mode volatility, and multi-tier opacity are structural features of the network. What remains optional is whether the response waits for a human to open a queue. The product that created the lag was insight without authority. The product that ends it is an agent allowed to spend a little money, inside a fence, before anyone is free to look.

See also: JD.com expands physical AI in logistics with 3 million robots

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The post Supply chains detect fast, act slow: How AI agents fix it appeared first on AI News.

JD.com is expanding AI and robotics across its logistics network under a new Physical AI Acceleration Plan, while reiterating a five-year target to procure 3 million robots, 1 million autonomous vehicles, and 100,000 delivery drones.

The company launched the plan at JDDiscovery 2026 in Beijing. JD Logistics also unveiled its industrial Wolf Robot series, designed for tasks across warehousing, sorting, transport, and delivery.

Specialised systems include equipment designed to operate in temperatures as low as minus 20 degrees Celsius, automated pharmacy dispatch systems, autonomous delivery vehicles, and drones.

The five-year procurement plan builds on automation that JD Logistics already has in operation. As of June 30, its LangzuTech Goods-to-Person automated warehousing system had been deployed in more than 30 warehouses across China, with deployments also launched in the UK and Germany.

JD Logistics’ broader warehouse network included more than 1,800 self-operated warehouses and more than 2,000 third-party cloud warehouses on its Open Warehouse Platform as of June 30. The network covered more than 36 million square metres in aggregate.

The company also had thousands of unmanned vehicles in regular operation across more than 20 Chinese provinces by the end of June. More than 100 domestic drone routes were operating across applications including parcel and food delivery, emergency medicine transport, and disaster relief.

From AI decisions to physical execution

JD Logistics is connecting its physical equipment with Meta Brain, an AI system used across warehousing, transportation, and delivery. JD said Meta Brain 3.0 can calculate optimal routes for hundreds of millions of parcels in seconds, compared with minutes previously.

Meta Brain also powers JD Logistics’ LangzuTech Packer robotic arm, which combines the model with multimodal sensor data to track, grasp, and place parcels with different shapes.

JD Logistics said in a first-quarter regulatory filing that the Packer uses parallel reinforcement learning in simulated environments to optimise parcel-placement sequences and loading layouts. The company said the system is designed to improve sorting efficiency and the use of available carrier space.

JD upgraded the robotic arm’s force-control technology during the second quarter to support more precise cage-loading operations. By June, the Packer was operating around the clock at multiple JD Logistics parks, according to the company’s interim report.

JD is also adding computing capacity to support AI development. JD Cloud plans to work with Chinese chipmaker Moore Threads on a cluster containing 100,000 GPUs for large-model training, inference, and embodied-AI workloads.

The two companies have previously worked on a 10,000-GPU cluster, according to Data Center Dynamics. Details of which Moore Threads GPU models will be used in the planned 100,000-GPU system have not been disclosed.

JD Cloud also plans to collect more than 10 million hours of video showing real-world human activities over the next two years for embodied-AI training.

Beyond warehouse operations, JD Logistics has expanded its autonomous vehicle network into night-time delivery. Its interim report said the company had launched night-time autonomous routes in Shenzhen, allowing vehicles to operate around the clock.

JD is also using drones in rural logistics. In June, JD Logistics launched a drone delivery network in Zizhong, Sichuan province, covering 78 administrative villages, and said deliveries to some mountain villages could be completed in as little as seven minutes.

Scaling automation across JD’s logistics network

JD did not disclose the total expected cost of the five-year procurement programme at JDDiscovery or provide a network-wide return-on-investment target.

JD Logistics spent RMB2.3 billion on research and development during the first half of 2026, up 23.7% from RMB1.9 billion a year earlier. The company attributed the increase to continued investment in technology and innovation but did not provide a breakdown showing how much was spent specifically on AI or robotics.

Depreciation of property and equipment and amortisation of other intangible assets rose 18.7% to RMB2.6 billion during the first half of 2026, from RMB2.2 billion a year earlier. JD Logistics attributed the increase mainly to additional logistics equipment and vehicles.

Purchases of property and equipment and investment properties totalled RMB3.09 billion over the same six-month period, compared with RMB2.70 billion a year earlier. Those figures cover the wider logistics business and are not disclosed as spending specifically associated with the new physical AI programme.

JD’s automation plans also come as China’s major ecommerce platforms expand fulfilment infrastructure. Reuters reported on September 3 that competition between JD.com, Alibaba, and Meituan had moved from heavy spending on delivery subsidies towards logistics infrastructure, broader supply, and order-level economics.

Alibaba and JD have been opening dark stores and fast-fulfilment “lightning warehouses” in densely populated areas to support deliveries within an hour, while Meituan has been building supermarkets to expand its grocery operations. Ministry of Commerce research cited by Reuters estimates China’s instant-retail market will reach RMB1.2 trillion, or about $178 billion, by the end of 2026.

JD and companies within its ecosystem employ around 700,000 delivery and logistics personnel, according to the South China Morning Post.

JD founder Richard Liu said earlier this year that robots would eventually take over parcel-delivery work now carried out by human couriers. The Financial Times reported in June that JD had signed agreements with around 120 educational institutions to retrain workers for roles including robot repair and maintenance.

JD said JD Logistics currently operates eight robot repair centres in China and plans to expand its robotics after-sales capabilities over the next five years. The company expects the expansion to support more than 100,000 robotics service engineer jobs.

(Photo by JD.com)

See also: Arm launches Total Design for Physical AI and robotics framework

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The post JD.com expands physical AI in logistics with 3 million robots appeared first on AI News.

Coca-Cola is using AI to recommend which products Malaysian retailers should order and in what quantities through its Coke Buddy platform.

The Perfect Basket feature uses Coca-Cola’s Central Recommendation Engine to analyse previous orders, ordering frequency, seasonality, weather, and purchasing patterns among similar businesses.

Coca-Cola said Coke Buddy currently supports about 39,000 retail outlets across Malaysia. The company describes Coke Buddy as a self-ordering platform that allows retailers to buy products through its app, website, or WhatsApp, with personalised order suggestions and order tracking also available.

Perfect Basket builds on those existing ordering functions by recommending both products and quantities before a retailer completes an order. Retailers can review the recommendations and retain control over what they purchase.

How Perfect Basket guides retailer orders

Coke Buddy already uses previous purchase history to suggest products a retailer is likely to order again. Perfect Basket adds other signals, including seasonality, weather, ordering frequency, and purchasing trends among comparable businesses.

Perfect Basket recommends products and quantities before retailers submit their orders through Coke Buddy. Fulfilment is handled separately by Coca-Cola Refreshments Malaysia or its suppliers under existing sales and distribution arrangements.

Coca-Cola said its sales teams remain involved with retailers alongside the digital ordering system, while retailers retain control over the final purchasing decision.

Coca-Cola recently disclosed usage figures for Perfect Basket following its Perfect Basket, Perfect Ride campaign, which ran from January to April 2026. The campaign encouraged retailers to use the recommendation feature when placing orders and received more than 4,500 entries from over 4,000 retailers in Malaysia.

During the campaign, 83% of participating outlets adopted Perfect Basket recommendations, according to Coca-Cola. The figure applies only to retailers taking part in the campaign, not the full network of about 39,000 outlets supported by Coke Buddy.

Coca-Cola also said participating outlets that followed the recommendations recorded higher sales revenue growth than comparable retail outlets. The company did not disclose the size of the difference or provide detailed performance data showing how individual recommendations affected sales or inventory levels.

The available Malaysian campaign data does not provide figures for forecast accuracy, stock availability, inventory levels, or logistics costs.

Suggested orders extend beyond Malaysia

Coca-Cola has deployed similar suggested-order capabilities elsewhere in its bottling network. In its first-quarter 2024 results, the company said it and its bottling partners had connected nearly eight million customers to B2B platforms globally, while AI-enabled suggested-order capabilities had reached more than three million outlets in Latin America.

Coca-Cola has said these systems combine customer data, external information, and AI to generate predictive order recommendations. Then-chief executive James Quincey said in 2024 that digital ordering also allows retailers to adjust deliveries without waiting for a salesperson to visit.

Coca-Cola has also linked suggested orders to changes in its sales process. Quincey said AI-generated orders allow pre-sales staff to spend less time taking routine orders and more time on account development, while retailers continue to make the final purchasing decision.

Coca-Cola has reported results from earlier pilots using similar recommendation systems. In its second-quarter 2024 earnings call, the company said retailers receiving AI-generated product recommendations based on previous orders and market data were more than 30% more likely to purchase the recommended SKUs in initial pilots. These results did not relate specifically to Perfect Basket in Malaysia.

In a separate demand-prediction project, Coca-Cola combined historical sales data with weather and geolocation information to generate replenishment recommendations. CIO Neeraj Tolmare told Fortune in 2025 that a three-country pilot recorded sales 7% to 8% higher than outlets that were not using the AI algorithm.

Perfect Basket remains available after the campaign. Coca-Cola said it plans to continue developing Coke Buddy and the recommendation feature using retailer feedback and data, while retailers will continue to have access to the company’s sales representatives alongside the digital ordering system.

(Photo by Mahbod Akhzami)

See also: MG Ship adds AI route optimisation as logistics returns accelerate

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The post Coca-Cola uses AI to improve retailer ordering in Malaysia appeared first on AI News.

MG Ship has introduced an AI route optimisation and carrier selection module as logistics deployments demonstrate rapid cost and time returns.

The technical module targets global retailers and commercial shippers, pairing automated routing algorithms with carrier recommendation systems across international trade corridors. The deployment arrives as enterprise supply chain operators report measurable operational returns from machine learning tools, moving capital allocations away from speculative trials toward production deployments.

Measurable returns from deploying AI for logistics

Suki Cheung, CEO of MG Ship, will present deployment metrics during a panel discussion at the upcoming WMX Asia conference. Cheung will join executives from Pos Malaysia, Omniva, and OnyX Space for the session, titled AI Beyond the Hype: Measurable Results in Logistics Today.

“Too many AI conversations in logistics remain focused on future possibilities,” said Cheung. “The reality is that AI is already delivering measurable business outcomes today. Leading organisations are reducing transportation costs, improving forecast accuracy, increasing warehouse productivity, and achieving payback within months rather than years.”

Industry operational data indicates that initial investment returns are concentrating across three primary workflows:

• Dynamic route planning has reduced enterprise fuel consumption by 15–20 percent, improved transit speeds by 15–25 percent, and lowered overall transportation costs by 12–22 percent, with capital payback reached within three to six months.
• Predictive demand forecasting has reduced projection errors by 20–40 percent, improved planning accuracy by up to 35 percent, and decreased excess inventory by 20–30 percent within six to 12 months.
• Automated freight documentation processing has cut manual task duration by up to 85 percent, recovering initial expenditure inside three to six months.

Over five-year deployment cycles, enterprise adopters have recorded average operational expense reductions between 10–25 percent, accompanied by warehouse productivity gains of 25–35 percent.

Routing algorithms and carrier scoring

MG Ship built the new routing capability directly into its visibility and supply chain intelligence platform, which serves retailers, manufacturers, and freight operators across multiple international markets. The base system synthesises live cargo telemetry with trade intelligence, risk monitoring, and predictive analytics to support operational planning and trade financing.

The route optimisation engine processes live and historical lane transit logs, weather patterns, air and ocean port congestion indicators, customs risk alerts, and transit reliability data. Shippers receive automated recommendations identifying low-cost, low-risk transit paths.

Carrier evaluation features rank transport providers per lane and service tier. Rather than selecting capacity purely on spot freight pricing, the system scores carriers against historical on-time metrics, transit consistency, exception occurrences, claims rates, available volume, and total cost-to-serve.

Logistics teams can also execute scenario simulations prior to peak shipping quarters. The software models lead times, service levels, freight spend, and risk exposures under alternative carrier allocation rules.

Early enterprise implementations demonstrate lower lead-time variance, reduced expedited freight expenditure, and improved on-time-in-full delivery rates.

Cheung stated that the platform “does not simply tell businesses where their cargo is”, adding that “it recommends the best route, the right carrier, and the lowest-risk option based on real-time conditions, helping organisations make faster and more profitable decisions.”

See also: OneRail uses Nvidia AI for real-time last-mile delivery optimisation

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The post MG Ship adds AI route optimisation as logistics returns accelerate appeared first on AI News.

OneRail has launched an AI-powered delivery platform that uses Nvidia technology to help retailers, wholesalers, and distributors decide how individual orders should be delivered.

Called OmniSTAR, the system evaluates options including owned fleets, couriers, parcel carriers, and other delivery modes, then selects the lowest-cost option that meets the required service level, according to OneRail.

The platform combines Nvidia’s cuOpt decision optimisation engine and cuDF data processing software with OneRail’s delivery pricing and performance data. Nvidia accelerated computing infrastructure is used to process the routing and delivery-mode calculations.

OneRail said the system can reduce computation times by as much as 10 times. A calculation that previously took 20 minutes can be completed in under two minutes, while a calculation taking a week can be reduced to about two days, according to the company.

OneRail said the shorter processing time allows the optimisation to run within live delivery operations, where multiple fulfilment options can be evaluated before an order is assigned.

“If you don’t have the ability to make lightning-fast decisions, you’re giving up margin,” Catania said in an interview with CNBC. “Last-mile fulfilment is expensive.”

From prediction to delivery decisions

OneRail’s broader AI systems use prediction and optimisation for different parts of the delivery process. The company said its machine-learning models estimate factors including service time, lateness risk, the probability of first-attempt delivery success, and expected price ranges.

OneRail said those predictions feed into optimisation systems that determine how an order should be executed. Separately, the company said OmniSTAR compares different fulfilment modes before selecting an option based on cost and service requirements.

Research on dynamic vehicle routing makes a similar distinction between predicting changing conditions and recalculating operational decisions as new information becomes available. A 2024 review in the European Journal of Operational Research identified travel-time prediction and real-time re-optimisation as separate areas of time-dependent routing research.

Nvidia cuOpt handles route optimisation

Nvidia describes cuOpt as an open-source, GPU-accelerated optimisation library for vehicle routing and other mathematical optimisation problems.

Nvidia’s documentation shows that cuOpt can account for vehicle costs, capacities, travel times, operating windows, starting locations, and other restrictions when calculating routes. Its cost models can also use distance, time, monetary cost, or a weighted combination of those measures.

OmniSTAR applies cuOpt to both routing and delivery-mode selection. OneRail said this allows the system to compare available fulfilment options for an order and identify the lowest-cost option that still meets its service requirements.

OneRail said many retailers still rely on static rules or manual planning when making these decisions, and that OmniSTAR is designed to evaluate more delivery combinations within shorter operational timeframes.

Nvidia said cuOpt does not exhaustively test every possible route. Instead, the solver generates candidate solutions and iteratively improves them using GPU-accelerated heuristics to produce high-quality results within a set computation time.

The platform also uses Nvidia cuDF, a GPU-accelerated library for tabular data processing, including filtering, joining, and aggregating datasets.

OneRail combines those capabilities with its own delivery data and operational models. Its dataset is based on millions of deliveries across a network that the company said includes more than 12 million drivers and over 1,000 logistics partners.

The data covers pricing and delivery performance across different transportation modes. OneRail said OmniSTAR can use the information to identify delivery rules that increase costs and assess how delivery choices affect item-level profitability.

The architecture disclosed for OmniSTAR centres on GPU-accelerated data processing and mathematical optimisation. Nvidia describes cuOpt as the optimisation component used for problems including vehicle routing.

Because cuOpt is stateless, changes in operating conditions require the optimisation problem to be modelled and submitted again. Nvidia cites vehicle breakdowns, driver absences, road blockages, traffic, and new high-priority orders as examples of changes that can prompt this type of dynamic reoptimisation.

OneRail said OmniSTAR can rerun delivery scenarios as variables including fuel costs, weather, and shipping conditions change. The company has separately said its use of cuOpt allows it to evaluate more routing scenarios and recalculate routes faster than its previous approach.

OmniSTAR moves into live operations

OmniSTAR is already deployed with selected enterprise customers.

At US Foods, OneRail said the system identified delivery configurations that were reducing margins, including low-margin products being transported long distances using higher-cost equipment. US Foods subsequently used the findings to adjust pricing and restructure some delivery patterns, according to OneRail.

OneRail also told CNBC that an unnamed large tire distributor using the platform achieved $40 million in run-rate savings over three years. The customer was not identified, and the savings figure was provided by OneRail. The company also told CNBC that it expects OmniSTAR to exceed $6 billion in gross merchandise volume during the fourth quarter of 2026.

CNBC reported that OneRail and Nvidia had worked on the project for three years before its launch. OneRail said the collaboration included direct engagement with Nvidia’s cuOpt engineering team on last-mile delivery and large-scale logistics optimisation, alongside its participation in the Nvidia Inception programme.

In March this year, FedEx launched FedEx SameDay Local in collaboration with OneRail, connecting customers to a national network of more than 1,000 delivery providers.

(Photo by Brecht Corbeel)

See also: A quarter of Nvidia’s business next year comes from labs it is financing

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The post OneRail uses Nvidia AI for real-time last-mile delivery optimisation appeared first on AI News.