Non classé
The AI Boom Is Becoming a Logistics Race
Published
2 mois agoon
By
For most of the past four years, the artificial intelligence race has been described as a software race. Who has the best model? Who has the largest model? Who has the best benchmark scores? Which model can reason, code or use tools most effectively?
Increasingly, those questions describe only the visible layer of the competition. Underneath the models is an enormous physical system: semiconductors, memory, networking equipment, servers, cooling systems, transformers, power generation, transmission capacity, fiber, land, construction labor and data centers. AI may be digital at the point of use. Its production system is intensely physical. And that means the AI boom is becoming a logistics race.
AI Has a Bill of Materials
Ask a consumer what ChatGPT, Claude or another AI system requires and the intuitive answer is computation. Ask a logistics professional and a more interesting picture appears. That computation has a bill of materials.
GPUs and specialized accelerators require semiconductor fabrication capacity. Those processors require advanced packaging and high-bandwidth memory. Servers require power supplies, racks and networking equipment. Data centers require switchgear, transformers, backup systems, cooling infrastructure and enormous construction programs. Then the entire system requires electricity.
At sufficient scale, the constraint moves from semiconductor manufacturing to power generation, grid interconnection, construction capacity and geography. Goldman Sachs Research now forecasts global data-center power demand rising roughly 170 percent between 2025 and 2030 and notes that grid-connection delays in parts of the United States can reach seven years.
That is not primarily a software problem. It is a capacity-planning and infrastructure-logistics problem.
The Constraint Keeps Moving
Complex logistics systems frequently exhibit a characteristic that operations managers know well: remove one bottleneck and another appears. AI infrastructure is demonstrating the same behavior.
First there were too few leading-edge GPUs. Semiconductor capacity expanded. Then advanced packaging became constrained. Then high-bandwidth memory emerged as another choke point. Then attention shifted toward data-center capacity, transformers, cooling and electricity.
Now large AI companies are effectively competing not simply for processors but for entire infrastructure ecosystems. Anthropic’s recent expansion illustrates the magnitude of the change. Reuters reports that after initially resisting huge infrastructure commitments, the company moved aggressively to secure compute as demand accelerated, including a reported $45 billion commitment involving British infrastructure provider Nscale alongside other major arrangements. The AI model may be the product customers see. The logistics network underneath it increasingly determines how much of that product can be delivered.
This Looks Familiar
There is a tendency to regard the AI buildout as unprecedented. Technologically, much of it is. Operationally, logistics has seen this movie before.
Industries that scale rapidly discover that theoretical demand and executable capacity are different things. A manufacturer can have customer demand without having components. A retailer can have inventory without having transportation capacity. A factory can possess equipment without having labor. A distribution network can possess warehouses without having sufficient throughput.
AI companies are discovering the same fundamental reality. Compute capacity exists only when dozens of dependent flows arrive in the correct place, sequence and configuration. A data center without transformers is not compute capacity. A rack without GPUs is not compute capacity. A GPU without memory is not compute capacity.
A completed building waiting seven years for grid interconnection is not compute capacity. Logistics professionals have always understood this instinctively: capacity is a system property. Owning one component does not mean the system can operate.
Network Design Comes to AI
This changes the strategic geography of AI. Data centers historically clustered around connectivity, demand and favorable economics. AI adds a different set of constraints: access to enormous quantities of reliable electricity, land, cooling resources, specialized construction talent, semiconductor flows and increasingly complex regulatory approval. Goldman expects the Mid-Atlantic to remain the largest U.S. data-center power market through at least 2030 but sees the Midwest moving into the second position ahead of Texas.
That is network design.
Where should capacity be located? How much redundancy is required? Which resources need to be colocated? What happens when transmission capacity becomes the bottleneck rather than compute? How much infrastructure should be owned and how much rented? How should demand be routed when capacity becomes constrained?
These sound increasingly like logistics questions because they are logistics questions. The underlying resource is computation rather than pallets, but the architecture is familiar.
Open Models Complicate the Equation
At the same time, demand is becoming more heterogeneous. One of the more interesting signals comes from Vercel’s AI Gateway. Open-weight models accounted for approximately 11 percent of token volume in April 2026, roughly 29 percent in June and hit about 62 percent on August 22. The precise mix will move around, but the direction is significant: users are increasingly routing workloads among multiple models rather than automatically sending everything to the most expensive frontier system. This begins to resemble transportation mode selection.
You do not use air freight for every shipment simply because it is fastest. You choose the service level appropriate to the requirement. AI workloads can increasingly be routed the same way.
A difficult reasoning problem may justify an expensive frontier model. A routine classification task may not. An enterprise may choose a smaller open model for predictable high-volume work, a specialized model for another application and a frontier system for the small share of problems requiring maximum capability. That turns AI architecture into an orchestration problem. The winner may not simply be the company possessing the best model.
It may be the company that can route enormous volumes of work across models and infrastructure at the lowest acceptable combination of cost, latency, reliability and capability. Again, that sounds remarkably familiar to logistics.
From AI Infrastructure to AI Logistics
The irony is that AI will increasingly optimize logistics while logistics becomes central to the development of AI. The industry is building one of the largest technology infrastructures in history. That infrastructure must be sourced, manufactured, transported, installed, powered, maintained and continually upgraded. It has suppliers, bottlenecks, capacity constraints, lead times, substitution problems and network-design decisions. In other words, it has a logistics architecture.
There is a larger lesson here for industrial companies. AI is often presented as if computation were infinitely elastic: send more work to the cloud and intelligence appears. The physical infrastructure underneath that abstraction tells a different story. Every digital action ultimately lands somewhere physical. That fact will shape cost.
It will shape geography.
It will shape competitive advantage. And eventually, AI itself will help orchestrate the enormous infrastructure system required to produce AI. The AI race began as a contest to build the most capable models. It is becoming a contest to build, supply and coordinate the system underneath them. That is a logistics race.
The post The AI Boom Is Becoming a Logistics Race appeared first on Logistics Viewpoints.
You may like
Non classé
Cutter & Buck, Kardex, and AutoStore: Why the Second Automated Warehouse May Matter More Than the First
Published
8 heures agoon
5 novembre 2026By
Warehouse automation announcements tend to celebrate the beginning. A new distribution center opens. Robots arrive. An automated storage and retrieval system goes live. Executives talk about throughput, labor savings, and future growth. But perhaps the more revealing warehouse automation story begins several years later, when a company decides whether to do it again. That is what makes the latest development involving Cutter & Buck, Kardex, and AutoStore particularly interesting.
Cutter & Buck, the apparel company, recently opened a new distribution center in Grand Prairie, Texas, featuring its second AutoStore goods-to-person system implemented by Kardex. The system includes 50,502 storage bins, 32 R5 Pro robots, and 13 ports, with Kardex FulfillX warehouse execution software coordinating the operation. It is designed for 1,256 bin presentations per hour, with simulation results averaging 1,284.
Those numbers are interesting. But the more important number may be two. This is not Cutter & Buck’s first attempt at warehouse automation. And that changes the story.
The First Warehouse Was the Experiment
Cutter & Buck began its earlier automation journey at its Renton, Washington, distribution center, where a Kardex-deployed AutoStore system went live in July 2023 with more than 34,000 bins and 54 robots. As the operation grew, the company later added another 9,962 storage bins along with additional ports and infrastructure. That evolution matters because the Texas facility was not designed simply by copying Renton.
According to Kardex, the companies used several years of operating experience, performance data, and lessons from the first deployment to shape the new distribution center around Cutter & Buck’s changing fulfillment requirements. This is where the story becomes much more useful for warehouse executives. There is an enormous difference between asking, “Should we automate?” and asking, “Knowing what we know after three years of automation, how should we automate the next building?” The second question may tell us far more about automation maturity.
What Happened After the Robots Arrived?
The publicly reported results from the original Renton deployment provide important context. In its case study, Kardex reports that Cutter & Buck reduced peak-season labor requirements by 60%, with 12 associates handling workloads that previously required 30. The company also reports that online orders can move from order to shipment in as little as 10 minutes.
Those are precisely the kinds of metrics automation buyers should be looking for. A warehouse can look highly automated without necessarily being highly productive. The business case ultimately depends on what happens to labor requirements, order cycle time, accuracy, capacity, service levels, and cost per order. That is especially important for Cutter & Buck because its fulfillment profile involves more than simply retrieving apparel from storage.
Nearly half of picking activity at the Renton operation supports embroidery workflows, according to the Kardex case study. That means products have to flow not just from storage to shipping, but through value-added customization processes as well. The Texas facility reflects that complexity. Its conveyor system connects AutoStore output with embroidery, order routing, Print & Apply, and shipping processes. In other words, the interesting technology story is no longer simply automated storage. It is automated flow.
The Second Warehouse Is the Real Vote of Confidence
Warehouse automation vendors understandably highlight initial customer wins. From an analyst perspective, however, repeat investment can be more revealing. A first implementation is based largely on expectations: projected throughput, anticipated labor savings, simulations, reference customers, and a calculated ROI. A second investment can be based on something much more valuable: the customer’s own operating data.
Cutter & Buck had several years to see how its first AutoStore deployment performed, expand that system as SKU requirements increased, identify where workflows could be improved, and then decide how to approach its next distribution center. It chose automation again. That does not mean every warehouse operator should reach the same conclusion. But it does suggest an important way customers should evaluate suppliers. Do not look only at how many new customers a technology provider wins. Look at how many existing customers expand, add robots or ports, increase storage capacity, or deploy the technology in another building. Those decisions occur after the sales presentation is over.
Modular Automation Changes the Economics
There is another important aspect of the Cutter & Buck example: scalability. At Grand Prairie, the number of robots and ports can be expanded independently as demand changes. That matters because one of the hardest parts of warehouse automation investment is forecasting the future.
Volumes change. SKU counts increase. Customer expectations shift. Peak seasons become more pronounced. A system designed perfectly for today’s demand may be poorly sized five years from now. Modular systems potentially give operators another way to think about ROI. Instead of asking whether they can perfectly forecast requirements over the entire investment horizon, they can ask whether the underlying automation architecture gives them enough room to respond when that forecast inevitably changes.
Cutter & Buck’s Renton facility illustrates that point particularly well. The original system was not frozen in its 2023 configuration. It subsequently expanded by nearly 10,000 additional bins as the business required more capacity. That ability to expand deserves more attention in automation business cases.
Stop Asking Only About Day-One Throughput
The warehouse automation market often focuses heavily on commissioning and initial throughput. Those metrics matter. But mature automation decisions should examine a longer timeline. What happened after year one?
Did the operation require more people than anticipated? What happened during peak? Could the system absorb additional SKUs? How expensive was maintenance? Could throughput be expanded without redesigning the facility? And most importantly, when the company needed another distribution center, did management choose the technology again? Research suggests these questions remain relevant. The 2026 Intralogistics Robotics Study surveyed 166 warehouse, distribution, and manufacturing professionals involved in robotic automation decisions. While 74% of organizations already deploying robots said they achieved their business goals, the study found a more complicated picture around cost and time to value.
That is an important distinction. Successful automation is not simply automation that works. It is automation capable of continuing to make economic sense as the operation changes.
Takeaway
The most interesting part of the Cutter & Buck, Kardex, and AutoStore story is not 32 robots or 50,502 bins. It is repeatability. Cutter & Buck implemented warehouse automation, operated it for several years, expanded it, learned from it, and then used those lessons to build another automated distribution operation. For companies evaluating AS/RS and goods-to-person technologies, that provides a useful lesson. Don’t just ask a supplier to show you its newest installation. Ask to see the customer that came back for the second one. That may be one of the clearest indicators that warehouse automation has moved from an impressive technology project to a sustainable operating strategy.
The post Cutter & Buck, Kardex, and AutoStore: Why the Second Automated Warehouse May Matter More Than the First appeared first on Logistics Viewpoints.
Non classé
Cutter & Buck, Kardex, and AutoStore: Why the Second Automated Warehouse May Matter More Than the First
Published
8 heures agoon
5 novembre 2026By
Warehouse automation announcements tend to celebrate the beginning. A new distribution center opens. Robots arrive. An automated storage and retrieval system goes live. Executives talk about throughput, labor savings, and future growth. But perhaps the more revealing warehouse automation story begins several years later, when a company decides whether to do it again. That is what makes the latest development involving Cutter & Buck, Kardex, and AutoStore particularly interesting.
Cutter & Buck, the apparel company, recently opened a new distribution center in Grand Prairie, Texas, featuring its second AutoStore goods-to-person system implemented by Kardex. The system includes 50,502 storage bins, 32 R5 Pro robots, and 13 ports, with Kardex FulfillX warehouse execution software coordinating the operation. It is designed for 1,256 bin presentations per hour, with simulation results averaging 1,284.
Those numbers are interesting. But the more important number may be two. This is not Cutter & Buck’s first attempt at warehouse automation. And that changes the story.
The First Warehouse Was the Experiment
Cutter & Buck began its earlier automation journey at its Renton, Washington, distribution center, where a Kardex-deployed AutoStore system went live in July 2023 with more than 34,000 bins and 54 robots. As the operation grew, the company later added another 9,962 storage bins along with additional ports and infrastructure. That evolution matters because the Texas facility was not designed simply by copying Renton.
According to Kardex, the companies used several years of operating experience, performance data, and lessons from the first deployment to shape the new distribution center around Cutter & Buck’s changing fulfillment requirements. This is where the story becomes much more useful for warehouse executives. There is an enormous difference between asking, “Should we automate?” and asking, “Knowing what we know after three years of automation, how should we automate the next building?” The second question may tell us far more about automation maturity.
What Happened After the Robots Arrived?
The publicly reported results from the original Renton deployment provide important context. In its case study, Kardex reports that Cutter & Buck reduced peak-season labor requirements by 60%, with 12 associates handling workloads that previously required 30. The company also reports that online orders can move from order to shipment in as little as 10 minutes.
Those are precisely the kinds of metrics automation buyers should be looking for. A warehouse can look highly automated without necessarily being highly productive. The business case ultimately depends on what happens to labor requirements, order cycle time, accuracy, capacity, service levels, and cost per order. That is especially important for Cutter & Buck because its fulfillment profile involves more than simply retrieving apparel from storage.
Nearly half of picking activity at the Renton operation supports embroidery workflows, according to the Kardex case study. That means products have to flow not just from storage to shipping, but through value-added customization processes as well. The Texas facility reflects that complexity. Its conveyor system connects AutoStore output with embroidery, order routing, Print & Apply, and shipping processes. In other words, the interesting technology story is no longer simply automated storage. It is automated flow.
The Second Warehouse Is the Real Vote of Confidence
Warehouse automation vendors understandably highlight initial customer wins. From an analyst perspective, however, repeat investment can be more revealing. A first implementation is based largely on expectations: projected throughput, anticipated labor savings, simulations, reference customers, and a calculated ROI. A second investment can be based on something much more valuable: the customer’s own operating data.
Cutter & Buck had several years to see how its first AutoStore deployment performed, expand that system as SKU requirements increased, identify where workflows could be improved, and then decide how to approach its next distribution center. It chose automation again. That does not mean every warehouse operator should reach the same conclusion. But it does suggest an important way customers should evaluate suppliers. Do not look only at how many new customers a technology provider wins. Look at how many existing customers expand, add robots or ports, increase storage capacity, or deploy the technology in another building. Those decisions occur after the sales presentation is over.
Modular Automation Changes the Economics
There is another important aspect of the Cutter & Buck example: scalability. At Grand Prairie, the number of robots and ports can be expanded independently as demand changes. That matters because one of the hardest parts of warehouse automation investment is forecasting the future.
Volumes change. SKU counts increase. Customer expectations shift. Peak seasons become more pronounced. A system designed perfectly for today’s demand may be poorly sized five years from now. Modular systems potentially give operators another way to think about ROI. Instead of asking whether they can perfectly forecast requirements over the entire investment horizon, they can ask whether the underlying automation architecture gives them enough room to respond when that forecast inevitably changes.
Cutter & Buck’s Renton facility illustrates that point particularly well. The original system was not frozen in its 2023 configuration. It subsequently expanded by nearly 10,000 additional bins as the business required more capacity. That ability to expand deserves more attention in automation business cases.
Stop Asking Only About Day-One Throughput
The warehouse automation market often focuses heavily on commissioning and initial throughput. Those metrics matter. But mature automation decisions should examine a longer timeline. What happened after year one?
Did the operation require more people than anticipated? What happened during peak? Could the system absorb additional SKUs? How expensive was maintenance? Could throughput be expanded without redesigning the facility? And most importantly, when the company needed another distribution center, did management choose the technology again? Research suggests these questions remain relevant. The 2026 Intralogistics Robotics Study surveyed 166 warehouse, distribution, and manufacturing professionals involved in robotic automation decisions. While 74% of organizations already deploying robots said they achieved their business goals, the study found a more complicated picture around cost and time to value.
That is an important distinction. Successful automation is not simply automation that works. It is automation capable of continuing to make economic sense as the operation changes.
Takeaway
The most interesting part of the Cutter & Buck, Kardex, and AutoStore story is not 32 robots or 50,502 bins. It is repeatability. Cutter & Buck implemented warehouse automation, operated it for several years, expanded it, learned from it, and then used those lessons to build another automated distribution operation. For companies evaluating AS/RS and goods-to-person technologies, that provides a useful lesson. Don’t just ask a supplier to show you its newest installation. Ask to see the customer that came back for the second one. That may be one of the clearest indicators that warehouse automation has moved from an impressive technology project to a sustainable operating strategy.
The post Cutter & Buck, Kardex, and AutoStore: Why the Second Automated Warehouse May Matter More Than the First appeared first on Logistics Viewpoints.
Non classé
Kinaxis Reports 22% SaaS Growth in Q3 2026, Raises Full-Year Outlook
Published
10 heures agoon
5 novembre 2026By
Kinaxis reported strong third-quarter 2026 financial results, with continued growth in cloud-based supply chain software, expanding customer adoption, and an increased full-year financial outlook. The results reflect sustained demand for supply chain planning and orchestration technology as manufacturers and distributors seek greater visibility, agility, and coordination across increasingly complex operations.
For the quarter ended September 30, Kinaxis generated $160.3 million in total revenue, an increase of 19% year-over-year. SaaS revenue rose 22% to $112.3 million, while annual recurring revenue (ARR) increased 19% to $483.9 million. Remaining performance obligations exceeded $1 billion, rising 21% from the prior year.
The company also reported $20.1 million in profit and adjusted EBITDA of $42 million, representing a 26% adjusted EBITDA margin.
Customer Growth Across Industries
Kinaxis continued to expand adoption of its Maestro supply chain orchestration platform across multiple industries and geographies.
New customer wins included Dr. Squatch, Sterlite Technologies, and WAI Global, along with organizations in luxury goods, biotechnology, and consumer health. The company also reported renewals and expansions involving Clariant and Sanofi.
More than 40 customer deployments went live during the quarter, including Asahi Beverages, with additional implementations across aviation, energy, healthcare, and manufacturing.
AI and Supply Chain Orchestration
Beyond its financial performance, Kinaxis is expanding its investment in AI-driven supply chain decision-making.
At Kinexions EMEA in October, the company introduced additional agentic AI capabilities, advanced inventory optimization, and enhancements to its Kinaxis Data Fabric. These developments are designed to help organizations move beyond traditional planning toward more connected and responsive operational execution.
The emphasis on AI-enabled workflows and improved inventory optimization reflects a broader industry transition toward systems that can identify changing conditions, evaluate alternatives, and support faster operational decisions.
Raised 2026 Outlook
Following its third consecutive quarter of outperformance, Kinaxis raised its full-year revenue guidance to $640 million–$650 million, increased its expected SaaS revenue growth to 20%–21%, and lifted its adjusted EBITDA margin outlook to 26.5%–27.5%.
The results highlight continued enterprise investment in supply chain planning technology, particularly platforms combining advanced optimization, real-time decision support, and emerging AI capabilities. For technology providers, the challenge increasingly extends beyond offering sophisticated planning tools to demonstrating how those capabilities translate into measurable operational improvements.
Read the full Kinaxis Q3 2026 financial results.
The post Kinaxis Reports 22% SaaS Growth in Q3 2026, Raises Full-Year Outlook appeared first on Logistics Viewpoints.
Cutter & Buck, Kardex, and AutoStore: Why the Second Automated Warehouse May Matter More Than the First
Cutter & Buck, Kardex, and AutoStore: Why the Second Automated Warehouse May Matter More Than the First
Kinaxis Reports 22% SaaS Growth in Q3 2026, Raises Full-Year Outlook
Freightos Global Freight Outlook – September 2026
Container rates jump another $1k/FEU – but is demand peaking? – July 8, 2026 Update
Walmart and the New Supply Chain Reality: AI, Automation, and Resilience
Trending
- Non classé2 mois ago
Freightos Global Freight Outlook – September 2026
- Non classé4 mois ago
Container rates jump another $1k/FEU – but is demand peaking? – July 8, 2026 Update
-
Non classé2 ans agoWalmart and the New Supply Chain Reality: AI, Automation, and Resilience
-
Non classé7 mois agoWhy Sulfuric Acid Is Emerging as a Supply Chain Constraint in Copper
- Non classé5 mois ago
Container rates starting to spike on peak season rush – June 2, 2026 Update
- Non classé1 an ago
13 Books Logistics And Supply Chain Experts Need To Read
- Non classé1 an ago
Ex-Asia ocean rates climb on GRIs, despite slowing demand – October 22, 2025 Update
- Non classé4 mois ago
LCL Shipping Cost Calculator: Calculate Air and Sea Shipping Freight Rates
