Traditional WMS Implementations were famous for two things: high costs and extreme rigidity. A simple process tweak meant submitting a ticket to vendor professional services and waiting months for custom code. For decades, evaluating a Warehouse Management System (WMS) has come down to a delicate balance between standardized functionality and operational realities. Software vendors promised out-of-the-box efficiency, but the moment a warehouse faced specialized picking sequences, unique compliance labels, or non-standard cross-docking workflows, those promises hit a wall. To navigate the pitfalls of their WMS, companies had two choices: adapt their physical processes to match the software’s rigid design or pay the vendor handsomely for custom code.

Custom code projects came with severe hidden costs: long development backlogs, fragile integrations, and high technical debt that turned future system upgrades into multi-million dollar nightmares. In todays volatile supply chain environment, combined with accelerated automation adoption, that old trade-off is obsolete. In response, the WMS landscape is undergoing a structural shift, moving away from rigid architectures and toward Low-Code/ No-Code (LCNC) extensibility.

De-Bunking the Jargon: Rule- Based Configuration vs. True Low-Code

“Flexible” and “adaptable” are among the most popular terms used by solution providers to market their updated Warehouse Management Systems. Vendors are racing to make their software solutions more adaptable and easier to customize. With almost all leading WMS solutions powered by AI, one size does not fit all. Customers’ supply chains vary in size and complexity; they also have access to a range of data sources, which can affect the breadth of features the system can leverage. To make WMSs more flexible and adaptable, there are two popular but very different architectural approaches to achieve these outcomes.

Rule-Based Configuration: Operating Within the Pre-Built Fence: Rule-Based Configuration allows users to adjust predefined settings, business logic, and parameters through administrative toggles, dropdowns, or rule engines.

What it looks like: A warehouse manager setting picking rules (“Prioritize FEFO over FIFO for perishable SKU category B”) or adjusting RF scanner display layouts from a set list of pre-configured fields.
The reality: Configuration is essential for daily management, but you are strictly operating inside a sandbox the vendor built for you. If an operational need requires a process flow, custom API connection, or database object that the vendor did not explicitly anticipate, configuration alone cannot solve it.

Low-Code/ No-Code (LCNC): Low-Code/No-Code provides a visual abstraction layer over the software’s underlying codebase. Instead of tweaking existing settings, LCNC empowers operations and internal IT teams to construct brand-new application capabilities without writing backend code line-by-line.

What it looks like: Using visual drag-and-drop builders to design a bespoke mobile returns-processing app, mapping custom data fields that auto-trigger external webhooks to a carrier system, or building visual automation flows for new Autonomous Mobile Robots (AMRs).
The reality: LCNC abstracts complex coding frameworks into visual components. It allows warehouse operators to extend what the system can do without altering the core codebase.

Rule-based configuration is like tuning the performance settings on a vehicle; you can adjust the suspension, gear shift timing, and dashboard displays. Low-code is like having modular chassis blocks; you can attach a trailer, add a new cargo bed, or reconfigure the body style entirely as your cargo changes.

Dimension
Rule-Based Configuration
Low-Code / No-Code (LCNC)

Primary Focus
Adjusting parameters within existing logic
Creating new application features and workflows

User Capability
Toggling pre-built operational switches
Building custom UI screens, data models, and triggers

Architectural Scope
Limited to vendor-defined scenarios
Extensible beyond original vendor design

Upgrade Impact
Zero impact on software upgrades
Extensions sit safely in an isolated abstraction layer

Why it Matters

The WMS market is on its way to eclipsing $10 billion by 2030, driven largely by rapid cloud migration and the growth of automated fulfillment centers. Within that growth, the underlying architecture of warehouse software is fundamentally changing. As major Tier 1 and Tier 2 WMS vendors push customers from legacy on-premises installations to cloud-native SaaS models, one of the biggest obstacles has been legacy customizations that do not transfer cleanly to cloud environments. By leveraging low-code/no-code (LCNC) abstraction layers, such as Manhattan’s Active Architecture and Blue Yonder’s Luminate platform extensions, custom business logic can reside above the core application layer. This allows vendors to deliver continuous cloud updates without disrupting customer-specific workflows. LCNC platforms also democratize application development by empowering business technologists, operations managers, industrial engineers, and systems analysts to build, test, and deploy workflow changes directly. As LCNC adoption expands, organizations can reduce their reliance on traditional development resources while enabling a broader range of operational experts to enhance and maintain platform functionality.

The major enterprise and mid-tier WMS Suppliers offering low-code/ no code platforms:

Vendor
Platform / Tool
Type of Low-Code Capability

Datex
Datex Studio / App Studio
Core WMS built directly on a native LCAP

Manhattan Associates
Manhattan Active Architecture
Microservice extensions & UI modifications

Blue Yonder
Luminate Platform
Workflow automation & API/data extensions

Softeon
Composable Engine
Modular workflow orchestration & WES rules

SAP
SAP Build / BTP
Drag-and-drop apps connected to SAP EWM

Locus Robotics
LocusONE
Visual endpoint mapping & robotics flows

This blog highlights select insights from ARC Advisory Group’s latest Warehouse Management Systems Market Map. The full research report is now available for purchase. To learn more about accessing the complete data set and market map, please contact Chanf@arcweb.com.

The post The End of Rigid WMS Deployments: Configuration vs. Low-Code appeared first on Logistics Viewpoints.

In 2008, Europe appeared to be in an enviable economic position. The European Union’s economy had reached roughly $19.3 trillion, making it approximately 30 percent larger than the $14.8 trillion U.S. economy when measured at prevailing exchange rates. Germany remained an industrial powerhouse, Britain was one of the world’s leading financial centers, and European companies held commanding positions in automotive manufacturing, industrial automation, chemicals, aerospace, transportation, and logistics.

Less than two decades later, that relationship has been reversed.

The International Monetary Fund’s April 2026 World Economic Outlook projects U.S. nominal GDP of approximately $32.38 trillion this year, compared with $23.03 trillion for the European Union. That puts the EU economy at roughly 71 percent of the size of the U.S. economy in nominal dollar terms.

The comparison requires some caution. Exchange rates affect nominal GDP measured in dollars, and the EU of 2026 is not identical to the EU of 2008 because the United Kingdom left the bloc. The chart therefore should not be interpreted as a simple measurement of underlying productivity. But the direction of travel is difficult to ignore. Over the past two decades, the United States and Europe have followed markedly different economic trajectories.

The explanations are familiar. Europe endured the sovereign debt crisis, Brexit, weak demographics, greater exposure to the post-2022 energy shock, and increasing industrial competition from China. Meanwhile, the United States has benefited from stronger technology investment, a large integrated domestic market, abundant capital, and more recently an extraordinary wave of spending associated with artificial intelligence. The IMF itself has highlighted strong U.S. productivity performance as one contributor to the economy’s recent resilience.

All of those factors matter. But for supply chain leaders, the divergence raises another, more provocative question: has decision velocity itself become a source of competitive advantage?

I believe it has.

When Physical Assets Stop Being the Only Constraint

For most of modern industrial history, competitiveness could be understood through a relatively familiar set of inputs. Labor mattered enormously. Capital determined which companies and countries could build factories and infrastructure. Energy availability and cost influenced where energy-intensive industries could operate competitively.

Those factors have not disappeared. A chemical plant cannot overcome uncompetitive energy economics with a better dashboard. A manufacturer cannot ignore labor availability, financing costs, or transportation infrastructure simply because it has implemented artificial intelligence.

But the industrial system has acquired another critical input: information.

Modern supply chains generate extraordinary volumes of operational data. Transportation management systems report shipment activity. Warehouse management systems track inventory movement. ERP platforms contain orders, production schedules, and financial transactions. Supplier portals, visibility networks, IoT devices, planning platforms, weather feeds, market data, and external risk services generate additional streams of information.

Twenty years ago, much of the problem was obtaining that information. Today, many large companies face almost the opposite problem. They can see enormous amounts of what is happening across their networks, yet translating all of those signals into coordinated action remains difficult.

The bottleneck is moving.

It is increasingly found in the distance between knowing something has happened and doing something useful about it.

The Monday Morning Problem

Consider an ordinary supply chain scenario.

A planner arrives Monday morning and learns that a critical inbound shipment will arrive two days late. A modern visibility platform may have identified the delay hours earlier. The event itself is no longer hidden.

What happens next is more revealing.

Someone must determine which production orders depend on the material. Inventory planners need to understand whether another facility has stock available. Procurement may need to contact the supplier. Transportation teams may evaluate expedited modes or alternate routes. Customer service needs to know which commitments could be affected. Finance may have to approve additional freight expense.

The company may possess every piece of information necessary to solve the problem, yet the response still travels through spreadsheets, emails, messaging applications, meetings, phone calls, and approval workflows.

A digital system detected the problem in seconds. The organization may require hours—or sometimes days—to decide what to do.

That gap deserves more attention.

I increasingly think of it as decision-to-action latency: the elapsed time between detecting an operational event and executing an appropriate response.

Supply chains already measure transportation lead time, dock-to-stock time, order cycle time, manufacturing cycle time, and countless other forms of latency. Yet the time consumed by organizational decision making is rarely measured with the same discipline.

It should be.

In a volatile supply chain, a theoretically optimal decision that arrives tomorrow may be less valuable than a sufficiently good decision made this afternoon.

Visibility Was Never the Destination

This is particularly important because the supply chain technology industry has spent much of the last decade pursuing visibility.

That investment was necessary. Transportation visibility platforms, control towers, supplier monitoring systems, warehouse technologies, IoT networks, and event-management platforms have dramatically improved the ability of companies to understand what is occurring across distributed operations.

But visibility was never supposed to be the destination.

It was infrastructure for better decisions.

Many companies are now discovering that more visibility does not automatically produce more responsiveness. In some cases, it produces additional alerts, exceptions, dashboards, and notifications that people must investigate.

The resulting paradox is striking: organizations can have near-real-time information flowing into processes that still operate at human administrative speed.

A shipment may be tracked continuously while the response to its delay waits for a meeting.

An inventory problem can appear immediately on a dashboard while the reallocation decision moves through several organizational layers.

A supplier risk may be detected automatically while people spend hours assembling the evidence required to decide what to do about it.

The problem is no longer simply information scarcity. It is decision scalability.

This Is Where AI Becomes More Interesting

Much of the discussion surrounding artificial intelligence still focuses on chatbots, document creation, copilots, and task automation. Those applications will create value, but I suspect they understate AI’s eventual importance to supply chains.

The larger opportunity is to compress decision cycles.

Return to the delayed shipment. An AI-enabled system could detect the disruption, identify the products and orders dependent on the shipment, calculate projected inventory exposure, evaluate alternate sources, compare transportation options, assess customer priorities, and recommend the most economically sensible response.

Within governed boundaries, some of those recommendations could eventually be executed automatically.

The transformation is therefore not simply from manual work to automated work. It is from slow organizational reasoning to much faster machine-assisted reasoning.

That distinction matters.

Enterprise technology originally gave companies systems of record. Later generations increasingly provided systems of visibility. We are now moving toward systems that participate in reasoning and decision making.

ARC’s work on AI in the supply chain points toward an emerging architectural foundation for this transition. Agent-to-Agent communication can allow specialized AI agents to coordinate across functional domains. Model Context Protocol can connect AI systems with enterprise tools and contextual resources. Retrieval-Augmented Generation can ground model responses in trusted enterprise knowledge, while Graph RAG can help AI reason across the relationships that characterize supply chains: supplier to component, component to product, shipment to facility, facility to customer.

The important point is not any individual acronym. It is what happens when these capabilities begin operating together.

A supply chain can increasingly move from merely sensing events toward understanding their significance, evaluating possible responses, coordinating activities across functions, and eventually executing some decisions within defined governance constraints.

That is a fundamentally different operating model.

Now Return to Europe

Viewed from this perspective, Europe’s competitiveness challenge becomes more nuanced.

Europe has not forgotten how to manufacture. It remains home to extraordinary industrial capabilities, deep engineering expertise, sophisticated infrastructure, and companies that lead globally in automation, automotive production, industrial equipment, chemicals, logistics, aerospace, and other complex sectors.

Energy is also unquestionably part of the problem. Europe’s industrial base has faced a difficult adjustment since Russia’s 2022 invasion of Ukraine disrupted an energy model that had provided significant parts of European industry, especially Germany, with access to relatively inexpensive Russian energy.

But energy primarily affects the economics of producing.

Decision velocity affects the economics of adapting.

And adaptation becomes increasingly important when the operating environment changes continuously.

A factory that produces extremely efficiently under stable conditions can still lose ground if competitors respond to changes in demand, supply availability, transportation capacity, customer behavior, or technology considerably faster.

This is why the U.S. technology ecosystem matters to an industrial discussion.

The United States has built enormous capabilities in cloud computing, enterprise software, data infrastructure, venture financing, semiconductors, and artificial intelligence. These technologies do not remain isolated inside the technology sector. They become inputs into the productivity of retailers, manufacturers, distributors, logistics providers, and virtually every other industry.

The connection between digital leadership and industrial leadership is becoming increasingly difficult to separate.

A Fourth Input to Competitiveness

For much of the twentieth century, industrial competitiveness was largely evaluated through labor, capital, and energy. Those three variables remain fundamental, but they no longer capture the entire picture.

A fourth belongs alongside them:

Decision velocity.

How quickly can an organization detect meaningful change? How quickly can it understand the consequences? How quickly can it evaluate alternatives? How quickly can it select and execute a response? And how quickly can the organization learn from the outcome and improve its next decision?

These capabilities affect almost every supply chain performance measure that matters.

Faster, better decisions can reduce inventory.

They can prevent stockouts.

They can improve asset utilization.

They can mitigate disruptions before those disruptions cascade through a network.

They can improve transportation decisions, sourcing decisions, production decisions, and customer-service decisions.

More importantly, the gains compound.

A manufacturer does not make one consequential decision each year. A large industrial enterprise makes thousands upon thousands of operational decisions every day. Improving the quality or speed of any one decision may have negligible financial impact. Improving thousands of them consistently can transform an operating model.

That is why decision velocity potentially matters beyond the enterprise.

Scale those improvements across hundreds or thousands of companies and what initially appears to be a software advantage begins to resemble an economy-wide productivity advantage.

The Next Productivity Gap

Globalization created one of the great productivity transformations of the modern era by allowing companies to reorganize manufacturing and sourcing around global differences in labor cost, production capability, and transportation economics.

Artificial intelligence could create another productivity gap through a very different mechanism.

Instead of moving work geographically, companies may increasingly compress time.

A planning process that previously required a week might take a day. A sourcing decision that took several days might be resolved in hours. A transportation exception requiring hours of investigation might be analyzed in minutes. Routine decisions that currently wait in an approval queue may increasingly occur automatically when predefined conditions are satisfied.

None of these changes sounds revolutionary in isolation.

That is precisely why their potential may be underestimated.

Supply chains consist of millions of decisions. Small reductions in decision-to-action latency, repeated continuously across planning, procurement, production, warehousing, transportation, and fulfillment, can accumulate into substantial productivity differences.

By 2035, I suspect we may view decision latency much differently than we do today.

We routinely measure the time a truck spends waiting at a facility. We measure warehouse dwell time. We measure manufacturing cycle time. We measure supplier lead time.

Eventually, executives may ask another question with equal seriousness:

How much time does our organization spend waiting to decide?

The Supply Chain of 2035

The answer will matter because the supply chain of the next decade is unlikely to be defined by one dominant application.

It will increasingly resemble a network of intelligent systems.

Some decisions will continue to be made entirely by people, particularly those involving strategy, ethics, unusual tradeoffs, or substantial financial consequences. Others will be generated by machines and approved by humans. Still others will become autonomous because the parameters and risks are sufficiently well understood.

The objective should not be maximum automation.

It should be maximum appropriate responsiveness.

That is an important distinction. The point of AI is not to eliminate people from supply chain management. Human judgment, institutional knowledge, negotiation, accountability, and creativity will remain essential.

The opportunity is to eliminate unnecessary latency surrounding those people.

Machines can gather the evidence.

Machines can monitor thousands of conditions simultaneously.

Machines can calculate downstream consequences.

Machines can evaluate routine alternatives.

Humans can concentrate their attention where human judgment creates the greatest value.

The resulting supply chain is not simply more automated. It is more adaptive.

The Lesson Behind the GDP Chart

This is what makes the U.S.–Europe GDP comparison interesting for supply chain leaders.

It would be too simplistic to look at the chart and declare that artificial intelligence, software, or decision velocity explains twenty years of economic divergence. It does not. The historical gap reflects a complex combination of demographics, exchange rates, fiscal and monetary policy, industrial structure, energy, investment, technology, Brexit, and other factors.

But the chart raises an important question about what determines the next twenty years.

Industrial heritage alone will not guarantee industrial leadership.

Neither will excellent infrastructure, inexpensive labor, abundant capital, affordable energy, or superior visibility.

Increasingly, competitive advantage will also depend on how quickly organizations can absorb information, understand what it means, make a decision, and act.

For supply chain executives, this makes decision-to-action latency more than another operational metric. It deserves to become a strategic one.

The last twenty years of supply chain technology helped companies see the world more clearly.

The next twenty years may be defined by how quickly they can respond to what they see.

And that may ultimately prove to be the more consequential transformation.

The post Europe’s Competitiveness Problem Goes Beyond Energy. It’s About Decision Velocity. appeared first on Logistics Viewpoints.

Weekly highlights

Ocean rates – Freightos Baltic Index

Asia-US West Coast prices (FBX01 Weekly) decreased 1%.

Asia-US East Coast prices (FBX03 Weekly) stayed level.

Asia-N. Europe prices (FBX11 Weekly) decreased 1%.

Asia-Mediterranean prices (FBX13 Weekly) decreased 2%.

Air rates – Freightos Air Index

China – N. America weekly prices decreased 2%.

China – N. Europe weekly prices increased 5%.

N. Europe – N. America weekly prices decreased 2%.

Analysis

After weeks of violent escalations in US-Iran tensions surrounding the status of the Strait of Hormuz, Iran and Oman may soon announce a bilateral agreement to reopen the waterway.

The deal would open the Hormuz – without tolls or fees on transiting vessels – for sixty days, with ships entering the Persian Gulf in coordination with Iran along the northern lane, and exiting in coordination with Oman via the southern lane.

Following the failed June Memorandum of Understanding, this agreement – which may not go into effect immediately and may be contingent on the US removing its blockade of Iranian ships – will attempt to create enough stability for renewed US-Iran negotiations toward an end to the conflict. But, by validating Iranian control over the strait, the deal would mark a significant de facto concession to Iran – despite serious earlier opposition from both the US and multiple Gulf states among others – and change to the pre-war status quo.

If the strait is reopened, the rebound in traffic will be gradual and, with the main central channel still closed due to Iranian mines, may not recover to normal levels under the new arrangement.

For the container market, more vessels will exit than enter at first, with long haul ships likely to stay away until carriers are confident this ceasefire is stable. The reopening should also ease some of the strain on the landbridge alternatives in the region, though carriers may be hesitant to send feeder vessels into the Gulf at first as well. If the reopening goes smoothly and contributes to progress in US-Iran negotiations – and if developments include a Saudi Arabia – Houthi deescalation – carriers may resume earlier cautious moves back toward Red Sea transits as well.

The biggest impact of a Strait of Hormuz reopening for logistics would be on oil prices. Crude prices had eased back to pre-war levels when the ceasefire took hold in late June and early July, but then shot up 35% and past $90 a barrel by late July. The recent de-escalation has prices down 18% since late July – only 10% above the baseline – and a reopening should push prices lower. Bunker prices that climbed 16% since early July have leveled off over the past two

weeks but are still 50% higher than before the start of the war. The resumption of crude flows should start putting downward pressure on refined products like bunker and jet fuel too, though the effect may not be immediate.

Even if oil prices ease in the near term, peak season supply-demand dynamics – not fuel costs – are the major drivers of container spot rate behavior for now.

Ocean peak season started early this year, with surging demand consistently pushing rates up across the major east – west lanes from late May through early July. BAF increases and manufacturer price hikes set for Q3 drove some of the frontloading, with some US shippers pulling peak season orders forward ahead of a late July tariff deadline.

But since early July – and despite planned GRIs and PSSs including for August 1st – rates on most of these lanes have eased or at least leveled off, suggesting that the frontloading-driven peak season rush was cooling earlier than usual too.

Asia – Europe rates decreased slightly last week, but dipped by another $500/FEU so far this week. Asia – N. Europe prices of about $5,000/FEU are down 14% from their July peak, with Asia – Mediterranean rates at $6,000/FEU, 16% below the July peak and about back to mid-June levels. Some carriers have additional significant increases slated for mid-August, but rate behavior over the last few weeks and reports of easing demand and increases in blanked sailings may make rate increases unlikely.

On the transpacific, East Coast rates have been stable at their peak level of about $9,000/FEU since early July. West Coast rates reached a peak of more than $7,500/FEU in early July and through last week had eased about 20% to around $6,000/FEU.

But West Coast daily rates so far this week have jumped back above $7,000/FEU on August 1st GRIs. NRF US ocean import volume projections last month estimated that demand in August would be well below July levels. But steady East Coast rates together with some forwarder reports of surprisingly strong demand and this recent West Coast rate bump may indicate that peak season strength is lasting longer than anticipated on the transpacific.

If these rate increases stick – or climb even higher on August 1st GRIs of $2,000 – $3,000/FEU – experts are offering multiple reasons for why peak demand may be holding up past the frontloading deadlines, including unexpectedly low inventory levels and stronger than anticipated consumer demand.

Another reason may be that the July 24th tariff deadline did not result in sharp tariff hikes. Many US shippers were frontloading peak season volumes ahead of the Section 122, 10% global tariff July 24th expiration date out of concern that duties could be higher soon after. Instead, Section 122 tariffs were immediately replaced by Section 301 tariffs on more than sixty trade partners – aimed at curbing forced labor imports – of 10% to 12.5% or about even with the expiring duties.

The USTR recently stated that its 301 investigation into excess manufacturing capacity by sixteen of the largest US trading partners is nearing completion. These tariffs could raise duty levels back to those set using IEEPA. But even once the USTR shares its findings, it will take several weeks before the president could implement the recommendations. This gap may be extending tariff frontloading by some shippers, likewise contributing to a longer than expected transpacific peak.

Finally, for all lanes – including Asia – Europe trades where consensus is that demand is cooling – rates may be facing upward pressure from supply side constraints as well, since two major typhoons struck Far East ports over the last few weeks. Typhoon Noul shut down ports in southern China in late July as regional hubs were still recovering from a mid-month storm. Some carriers are now skipping Shanghai port calls as congestion remains severe there, with multi-day delays also reported in Ningbo, Shenzhen and Hong Kong.

In air cargo, some carriers have announced increases in fuel surcharges for August as jet fuel prices that have leveled off in the last couple weeks remain 33% higher than a month ago. For now though, global prices have continued their slow season slide with the Freightos Air Index global benchmark down 8% compared to the end of June.

China – US rates eased 2% last week to $5.67/kg. And though China – Europe prices climbed 5% to $4.02/kg last week, they remain more than 10% lower than a month ago, as the end of de minimis in the EU has led to lower volumes and rates on this lane even as carriers shift capacity to higher demand origins like Taiwan, where AI hardware is keeping volumes elevated.

The post Transpac peak may stretch on even as Asia – Europe ocean cools – August 6, 2026 Update appeared first on Freightos.