Connect with us

Non classé

The Hidden Infrastructure of Logistics: Why Cohesion Matters

Published

on

Logistics has always depended on infrastructure. Warehouses, ports, transportation networks, roads, inventory systems and increasingly sophisticated software form the visible architecture through which goods move. But some of the most important logistics infrastructure is almost invisible.

It exists between organizations, systems and decisions. It is the common understanding that allows a warehouse management system to exchange meaningful information with a transportation management system. It is the standard operating process that allows a carrier, shipper and distribution center to respond to the same disruption without three different interpretations of what just happened. It is the architecture that turns independent components into a functioning system. I think of that infrastructure as cohesion.

And as logistics becomes more automated, distributed and intelligent, cohesion may become considerably more valuable.

Coordination Has a Cost

We usually think about logistics cost in familiar terms: transportation, inventory, warehousing, labor and increasingly energy. There is another cost that appears almost everywhere but rarely receives its own line on the income statement. It is the cost of coordination.

Every handoff creates some amount of friction. A planner communicates with a carrier. A carrier communicates with a distribution center. A warehouse receives information from an ERP system. A transportation management system receives information from the warehouse. A control tower receives data from all of them and attempts to determine whether what was planned is still what is happening.

When the architecture is coherent, these exchanges become relatively inexpensive. Data carries consistent meaning. Systems understand one another. People know who owns the decision. Exceptions move toward resolution rather than sideways through an organization.

When cohesion is weak, coordination becomes expensive. People reconcile spreadsheets. Analysts investigate conflicting timestamps. Managers join conference calls to determine which system is correct. Employees learn workarounds because the formal process does not accurately describe how the operation actually functions.

A company can possess world-class individual systems and still operate a mediocre logistics network because the connections between those systems are weak.

Connectivity Is Not Cohesion

This distinction matters because logistics organizations have spent enormous amounts of money becoming connected.

APIs connect applications. EDI connects companies. Visibility platforms aggregate events. IoT devices generate telemetry. Cloud architectures make information available almost everywhere.

Yet connectivity alone does not create coherence.

Two systems can exchange data and still disagree about what that data means. Ten organizations can see the same shipment and still operate under different objectives, escalation rules and decision rights. A control tower can display an exception beautifully without possessing the authority or context required to resolve it.

The problem is not simply whether the nodes are connected. It is whether the network has enough shared architecture for those nodes to behave coherently.

That means common definitions, data structures, interfaces, operating rules, decision rights and performance measures. It also means understanding dependencies. A change to a labor policy in a warehouse may affect trailer dwell, carrier performance, transportation cost, customer service and inventory positioning. Optimizing one node independently can degrade the system. This is why logistics increasingly looks less like a collection of functions and more like an engineered system.

Standards Are Coordination Infrastructure

Standards are sometimes treated as tedious technical necessities. In reality, they are one of the mechanisms through which large systems acquire cohesion.

A standard does not require every participant to become identical. It establishes enough common language for different participants to work together.

Containerization may be the most obvious physical example. The standardized shipping container did not make ships, ports, railroads and trucks identical. It created a shared physical interface between them. The enormous productivity improvement came not from optimizing any one transportation mode, but from reducing the friction between modes. The same principle applies to digital logistics.

Standardized identifiers, messages, event structures, APIs and business semantics reduce the amount of translation required between systems. Shared operating processes reduce ambiguity between organizations. Explicit decision rights reduce the amount of organizational negotiation required when something changes.

Cohesion is therefore not sameness. It is interoperability with enough shared structure to support coordinated action.

That distinction is important. Logistics networks require specialization. Carriers should not behave like warehouses, warehouses should not behave like manufacturers, and planning systems should not behave like execution systems. The goal is not homogenization. The goal is to allow specialized components to contribute to a larger system without forcing people to continuously rebuild the connections between them.

From Connectivity to Architecture

This is also why I believe systems engineering has become increasingly relevant to logistics. In ourSystems Engineering in Logisticswork at Logistics Viewpoints, we have argued that logistics leaders need to think beyond individual applications and functions. Business requirements, process architecture, data architecture, decision architecture, technology, human roles, controls and measurable outcomes have to fit together.

That is fundamentally a cohesion problem.

Historically, logistics could tolerate a surprising amount of fragmentation because humans supplied the missing integration layer. Experienced planners understood which data could be trusted. Warehouse supervisors knew which system messages could safely be ignored. Transportation managers knew which carriers required a phone call rather than an automated tender. Humans carried enormous amounts of undocumented architecture in their heads.

That arrangement does not scale well into highly automated logistics. As autonomous mobile robots, warehouse automation, machine-learning models, decision-intelligence systems and AI agents assume more operational responsibility, implicit coordination must increasingly become explicit architecture. Machines are less forgiving of ambiguity than experienced operators.

AI Raises the Stakes-and the Opportunity

AI makes the cohesion question considerably more interesting. Much of the first generation of enterprise AI has been layered on top of existing processes. The AI recommends something; a human interprets the recommendation and decides what to do.

Agentic systems move one step further. They can potentially detect an exception, assemble context, evaluate alternatives, select an action, communicate with another system and monitor the result.

That is a very different operating model.

It also means that poorly integrated logistics architectures will become increasingly visible. An agent cannot reliably orchestrate an enterprise if customer priorities live in one system, inventory truth in another, transportation constraints in a third, and unwritten business rules remain in the heads of experienced employees.

AI therefore does not eliminate the need for architecture. It increases it.

But the opportunity is equally large. Once a logistics network has sufficient cohesion, AI can operate across connections that were previously too numerous and dynamic for humans to manage continuously. It can coordinate transportation with warehouse capacity, inventory with customer commitments, production with material availability and execution decisions with rapidly changing conditions. The network can begin responding as a system.

The Friction Between the Nodes

For decades, logistics technology has primarily improved the nodes. Better WMS. Better TMS. Better planning. Better automation. Better visibility.

Those investments still matter.

But increasingly, competitive advantage may come from improving what exists between the nodes. The company that can make good decisions inside a warehouse has an advantage. The company that can make good decisions across warehouses, transportation, inventory, suppliers, customers and automated systems has a larger one.

That requires cohesion.

The next generation of logistics performance will not come simply from connecting more things. Almost everything is already being connected. It will come from making those connections coherent enough that the entire network can understand, decide and act. Cohesion is the infrastructure between the nodes. AI may become the intelligence that allows those nodes to finally operate as a system.

The post The Hidden Infrastructure of Logistics: Why Cohesion Matters appeared first on Logistics Viewpoints.

Continue Reading

Non classé

Cutter & Buck, Kardex, and AutoStore: Why the Second Automated Warehouse May Matter More Than the First

Published

on

By

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.

Continue Reading

Non classé

Cutter & Buck, Kardex, and AutoStore: Why the Second Automated Warehouse May Matter More Than the First

Published

on

By

Cutter & Buck, Kardex, And Autostore: Why The Second Automated Warehouse May Matter More Than The First

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.

Continue Reading

Non classé

Kinaxis Reports 22% SaaS Growth in Q3 2026, Raises Full-Year Outlook

Published

on

By

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.

Continue Reading

Trending