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The AI-Powered Operating Layer Has Arrived, and Your Supply Chain Is Where It Starts
Published
2 mois agoon
By
A year ago, when people talked about AI in supply chain, they mostly meant chatbots that could answer questions about shipment status or generative models summarizing reports. Useful stuff, but incremental. That’s changed fast.
What’s emerged over the past twelve months is a different class of AI altogether. AI agents can now execute multi-step workflows autonomously, coordinating across systems, making decisions based on real-time data, and acting on those decisions without waiting for a human to click “approve.” They read shipping documents, cross-reference contracted rates, flag discrepancies, and initiate dispute processes. They monitor inbound shipments, detect delays, adjust dock schedules, and notify downstream teams. They do this continuously, across thousands of transactions per week.
I don’t want to belabor the point here. If you’ve been paying attention to reports like the Bain Technology Report or McKinsey’s State of AI survey, you already know the trajectory. The technology is real. The harder question for logistics and supply chain leaders is what it means for how their organizations operate.
The Opportunity: Collapsing Operational Silos
Here’s the argument I want to make plainly: An agentic AI operating layer, built on supply chain data, will collapse the organizational silos that have defined how large shippers run their businesses for decades.
The technology isn’t magic. Supply chain data happens to be the connective tissue between departments that have historically operated as if they had nothing to do with each other.
Finance needs delivery confirmation to trigger early payment discounts. Procurement needs carrier performance data to update scorecards. Customer service needs real-time order status to respond to penalty claims. Production planning needs inbound ETAs to adjust manufacturing schedules. Insurance needs shipment documentation to process claims.
All of these decisions happen in different departments, in different systems, managed by different teams. But they all start with real-time data about shipments, orders, inventory, and deliveries.
For years, the handoffs between “supply chain knows something” and “another department acts on it” have been manual. Someone pulls a report. Someone else verifies it. A third person takes action in a different system. That’s how most companies still operate. And most of the time, it’s a reaction to a disruption rather than proactive alignment across functions.
An AI operating layer changes that equation. When agents can ingest supply chain data in real time, apply business rules, and execute actions across enterprise systems, those manual handoffs disappear. A delayed inbound shipment doesn’t wait for someone to notice it in a report and then email the warehouse. The agent detects the delay, recalculates the dock schedule, and notifies the facility team before anyone opens a spreadsheet.
Supply Chain Data as a Trigger
At FourKites, we’ve deployed AI agents that handle specific operational functions autonomously. One monitors shipments around the clock, investigates delays, and coordinates with carriers. At Coca-Cola, it cut response times for “where’s my truck” queries from 90 minutes to seconds. Another handles supplier collaboration, reading shipping documents and creating tracking records automatically. A third manages customer and vendor scheduling, reducing team workload by half at facilities like US Cold Storage.
But the more interesting development is what happens when you extend beyond traditional logistics workflows. Things like automatically validating freight invoices against contracted rates and actual service levels. Or accelerating payment cycles by identifying early discount opportunities tied to delivery confirmation.
More than “visibility” use cases, these automations extend to finance, procurement, warehouse operations, and customer service. But they all depend on supply chain data as the trigger. This is increasingly how leading shippers are thinking about their technology stack — connecting supply chain platforms directly to ERPs, CRMs, and financial systems so that operational data can trigger action in those systems without manual intervention. Gartner’s 2025 Supply Chain Top 25 highlighted this move toward autonomous, cross-system orchestration as one of the defining characteristics of the highest-performing supply chains globally.
The workflow executes in another function, but the intelligence that drives it originates in the supply chain. That’s what makes supply chain the starting point for an enterprise-wide AI operating layer, not the boundary of it. So the question becomes what it takes to actually stand up an operating layer like this.
What’s Required to Build It
Let me be honest about what it takes, because I think there’s been too much hand-waving in the market about AI transformation.
Start with the data foundation. An operating layer is only as good as the data flowing through it. For shippers, that means having a real-time view of what’s happening across your supply chain network, not a batch-updated dashboard that’s six hours stale. You need live shipment status, carrier performance history, order-level tracking, facility throughput data, and the system integrations to connect it all. If your data is fragmented across disconnected point solutions, the AI has nothing meaningful to work with.
Focus on proven workflows, don’t automate broken ones. This is the hardest part, and it’s where most companies stall. McKinsey’s 2025 State of AI survey found that 88% of organizations now use AI in at least one business function, but only about 6% are capturing meaningful enterprise-wide value from it. The biggest differentiator between those groups is workflow design. For example, a freight invoice audit that currently involves three people touching a spreadsheet could be replaced by an agent that cross-references the contracted rate, validates the service level against tracking data, and flags only genuine discrepancies for human review.
Build for orchestration across systems, not within one system. Here’s where the general-purpose AI platforms fall short. Many of them are good at connecting to your systems and building automations for whatever you throw at them. But they don’t have context from an external network that reveals impacts to your operations. They start with your data alone.
A supply chain operating layer starts with your data plus the operational intelligence from a broader network: which carriers perform well on which lanes, how delays in one region tend to ripple to facilities in another, and what distinguishes a genuine exception from normal variability. That context is what allows agents to act, not just surface alerts.
The Pace of Change
I also want to acknowledge something that too many people are glossing over. This stuff has moved unbelievably fast. The industry has been talking about AI agents for over a year now, but they’ve only become truly viable in production settings in the past few months. The underlying model capabilities, the integration tooling, the orchestration frameworks. All of it has matured at a pace that’s genuinely difficult for any organization to keep up with.
Jason Lemkin at SaaStr recently described what’s happening in enterprise software as a structural budget reallocation. IT spending is growing modestly overall, but AI budgets are absorbing a disproportionate share. Application counts are flat. Seat-based growth is under pressure. Companies aren’t spending more on software. They’re spending differently, and they’re spending on outcomes.
For supply chain automation specifically, you don’t need a multi-year transformation program to get started. The modular architectures that exist today make it possible to deploy production-grade agents in weeks rather than quarters. And platforms like FourKites’ Loft now make it possible to build and configure AI agents around your specific business rules, SOPs, and system integrations — not a one-size-fits-all workflow.
But to get the most ROI, you must first understand the workflows that consume the most manual effort and document the SOPs that govern how your teams handle exceptions, validate data, and communicate across functions. That’s the raw material that AI agents need to operate effectively.
The technology is ready. Whether your organization has done the foundational work to take advantage of it is a different question, and it’s the one worth spending time on.
By Matt Elenjickal, CEO, FourKites
The post The AI-Powered Operating Layer Has Arrived, and Your Supply Chain Is Where It Starts appeared first on Logistics Viewpoints.
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The Supply Chain Cost Stack: Where Margin Is Actually Engineered
Published
2 heures agoon
6 avril 2026By
Costs are rising again. That part is familiar. What is less clear, and more important, is where margin is actually won or lost inside a supply chain. It is not at the line item level. It is in how decisions play out across the system.
Where Cost Programs Start and Stall
When costs rise, most organizations go to the same places first. Transportation. Procurement. Warehousing. That is where the pressure is visible, and where teams are expected to respond.
Transportation renegotiates rates. Procurement pushes suppliers. Operations looks for incremental gains. Each function does its job and usually finds something. But the overall cost position does not move nearly as much as expected.
This is a pattern. It shows up across industries and across cycles.
The issue is not effort. It is structure. Supply chain margin is not determined inside any one function. It is shaped by how decisions interact across functions, often in ways that are not fully visible when those decisions are made.
The Stack, Not the Category
Supply chains operate as a stack of linked decisions. Not a collection of independent cost centers.
Network design sets the footprint. Sourcing defines cost and exposure. Inventory policy determines how much buffer exists in the system. Transportation turns plans into movement. Fulfillment is where cost and service finally meet the customer.
These are tightly connected. Change one, and something else moves.
A lower unit cost from a more distant supplier often increases transportation exposure. A network designed for speed tends to carry more inventory. A transportation savings initiative can introduce service variability that shows up later, usually as exception cost.
Most inefficiency does not sit neatly inside a function. It lives in the seams.
What Actually Moves Margin
In practice, margin moves in a few predictable ways.
Trade-offs are one. Cost and service are often optimized in different parts of the organization without a shared view. That leads to overperformance in some areas and unnecessary cost in others. It is rarely intentional.
Variability is another. Delays, disruptions, demand swings. These introduce cost that does not show up in standard models but accumulates quickly through expedites, rework, and recovery efforts. In many networks, this is where margin quietly erodes.
Then there is timing. Decisions are often made too early. Planning cycles lock in assumptions that no longer hold by the time execution begins. From there, the system spends the rest of the cycle adjusting. Usually at a higher cost.
This is less about modeling accuracy and more about when decisions are made.
What Is Changing
What is changing, gradually but clearly, is where decisions are being made.
In some operations, decision making is moving closer to execution. Routing is adjusted during the day. Carrier selection is not fixed for long. Inventory moves in response to conditions, not just plans. Exceptions are handled as they happen.
Not everywhere. But enough to notice.
The difference shows up in small ways at first. Less rework. Fewer expedites. Fewer surprises late in the cycle. Over time, it adds up. The gap between plan and outcome narrows, and that is where margin starts to appear.
The Role of Technology
Technology plays a role here, but it is not the story on its own.
Better decisions require coordination and speed. That is difficult with static systems and fragmented data. What is improving is the ability to process current conditions and adjust without waiting for a full planning reset.
In some environments, that is supported by AI and advanced analytics. In others, it is driven by process discipline and better visibility. Either way, the common thread is shorter distance between signal and response.
What to Watch
A few things tend to separate stronger operators from the rest.
One is coordination. Whether sourcing, transportation, and inventory decisions are made with a shared understanding of cost and service.
Another is response speed. How quickly the organization adjusts when something changes.
And then there is visibility. Whether trade-offs are understood when decisions are made, or only discovered later through cost and service misses.
These are not abstract measures. They show up in day-to-day performance.
Closing Perspective
Cost pressure is not new. Most organizations know where their major cost categories sit.
What is changing is how those costs are managed. Margin is not coming from isolated savings initiatives as much as it once did. It is coming from better coordination, better timing, and fewer corrections during execution.
That is harder to see than a rate reduction. It is also where most of the improvement is coming from now.
The post The Supply Chain Cost Stack: Where Margin Is Actually Engineered appeared first on Logistics Viewpoints.
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Nike and the Converse Question: Operate or Orchestrate the Asset
Published
3 jours agoon
3 avril 2026By
A declining brand inside a strong portfolio highlights a familiar supply chain decision: optimize the node, or change the operating model
A Portfolio Decision, Not a Brand Problem
Nike does not have a brand problem with Converse. It has a decision to make.
Converse has been losing ground for some time. Sales are down, investment has been pulled back, and the brand remains tied to a narrow product base that no longer carries the same weight in the market. At the same time, Authentic Brands Group has shown interest in acquiring it.
That combination is usually a signal. Not of failure, but of misalignment.
When an Asset Starts to Drift
Inside a large portfolio, most assets do not fail all at once. They drift. Performance weakens, attention shifts elsewhere, and the asset becomes harder to justify in its current form. The instinct is to stabilize it. Reduce cost. Adjust leadership. Try to recover momentum.
Nike is following that path.
But there is a second option. One that shows up often in supply chain decisions, though it is rarely framed that way.
The Supply Chain Analogy
When a node in a network underperforms, you can try to improve it where it sits. Or you can change its role in the system.
Converse looks less like a turnaround candidate and more like a node that no longer fits cleanly within Nike’s operating model. It is concentrated around a single product, lacks a strong innovation pipeline, and is not fully aligned with how demand is evolving. These are not surface issues. They are structural.
Supply chains see this pattern in different forms. A distribution center that once made sense but now sits outside the optimal network. A supplier that was once reliable but cannot keep pace. A lane that no longer supports the required service levels. In each case, the question is the same. Improve it, or reposition it.
Two Paths: Operate or Reposition
Nike is choosing to operate the asset. That means continued internal ownership, continued integration, and a requirement to restore growth within the existing structure.
Authentic Brands would take a different approach. The brand would be separated from execution. Manufacturing, distribution, and retail would be handled through partners. The asset would not be fixed. It would be redeployed.
That model is not unique to fashion. It is increasingly visible across supply chains. Some organizations continue to own and operate end to end. Others are moving toward orchestration, managing networks of partners rather than controlling every node directly.
Cost Control Is Not Structural Change
The distinction matters because it changes where value is created.
In an integrated model, value depends on how well each part performs and how tightly those parts are aligned. In an orchestration model, value comes from coordinating a network that can adapt more quickly than any single operator.
Nike’s current actions focus on cost. That is a reasonable first response. But cost control does not change the role of the asset. It keeps the system stable without addressing whether the system itself still makes sense.
Supply chain leaders see this often. Optimization is applied to a network that should be redesigned. The result is incremental improvement where structural change is required.
Where Control Is Moving
The more important signal sits above the brand itself.
Across industries, control is shifting. Away from physical ownership and toward coordination. Away from managing individual assets and toward managing how those assets work together. In supply chains, this shows up in platform models, in partner ecosystems, and increasingly in systems that optimize across networks rather than within them.
Bottom Line
The Converse question sits directly in that shift.
Nike can continue to operate the asset and work to restore its place within the portfolio. Or it can acknowledge that the asset may perform better in a different model, one built around orchestration rather than ownership.
That decision is not unique to Nike.
It is the same decision showing up across supply chains.
Operate the network, or orchestrate it.
The post Nike and the Converse Question: Operate or Orchestrate the Asset appeared first on Logistics Viewpoints.
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Supply Chain and Logistics News (March 30th- April 2nd 2026)
Published
3 jours agoon
3 avril 2026By
This week’s top stories in supply chain and logistics reflect the rate at which market dynamics shift. Two major railord companies are merging, focusing on enhancing supply chain reliability through reduced handoffs. The World Food Programme reports that the Strait of Hormuz blockage is causing a supply chain disruption that eclipses the impact of the Covid-19 pandemic. Logistics managers’ salaries are reported to be increasing in this year’s salary survey, and Sysco bids to purchase Restaurant Depot.
Your Top Supply Chain & Logistics Stories for the Week:
Union Pacific- Norfolk Southern Merger Leaves the Station
The proposed merger between Union Pacific and Norfolk Southern aims to create a transcontinental rail network by integrating the two systems with minimal geographic overlap. According to Union Pacific, the strategy focuses on enhancing supply chain reliability through reduced handoffs, a larger shared pool of locomotives and crews, and a unified customer service system. To avoid the operational disruptions associated with past industry consolidations, the companies are utilizing real-time diagnostics and digital development environments to simulate network changes before implementation. This end-to-end integration is designed to streamline existing interchange points and provide a more resilient infrastructure capable of recovering quickly from external shocks such as labor volatility or extreme weather.
The World Food Programme (WFP) reports that conflict in the Middle East, specifically regarding the Strait of Hormuz, has caused the most significant global supply chain disruption since the COVID-19 pandemic and the onset of the war in Ukraine. Approximately 70,000 metric tons of food aid are currently delayed or immobile due to port congestion and vessel idling. To mitigate these risks, shipments are being rerouted around Africa, a move that adds 25 to 30 days to transit times and increases shipping rates by 15% to 25%. While the WFP has managed to avoid $1.5 million in additional costs through negotiated waivers, the agency warns that rising prices and logistics hurdles could contribute to an additional 45 million people facing acute hunger by June 2026.
2026 Salary Survey for Logistics Management Reaches New Heights
The 2026 Salary Survey from Logistics Management reports that average annual salaries reached $126,400 as the profession transitions from a back-office operational role to a strategic business driver. This compensation growth is primarily fueled by a significant expansion in responsibilities; 76% of professionals now oversee complex functions, including technology investment, global risk management, and C-suite-level strategy. As companies increasingly view supply chain expertise as a “strategic interface” essential for revenue generation rather than a mere cost center, the market value for these leaders has climbed, with 57% of respondents receiving an average raise of 7% this year.
Sysco’s Bid for Restaurant Depot: Distribution Control Is Shifting
The proposed $29.1 billion acquisition of Jetro Restaurant Depot by Sysco represents a strategic pivot from traditional broadline delivery to a multi-channel “access network” model. By internalizing the industry’s primary cash-and-carry pricing benchmark, Sysco effectively absorbs a critical market check, consolidating pricing power and gaining granular visibility into the real-time purchasing behaviors of over 700,000 independent operators. This structural shift allows for sophisticated margin optimization by routing volume through the most cost-effective channel—leveraging Restaurant Depot’s warehouse model to eliminate last-mile logistics expenses, which typically account for one-third of total distribution costs. Ultimately, the deal moves beyond mere scale, positioning data-driven network design as the new dominant competitive advantage over traditional route density.
Global Energy Regulation Round Up Q1 2026
The Global Energy Regulation Round Up is a quarterly report covering energy regulations worldwide. It is organized into three regions: North America, the European Union, and Asia. Click the link to download the full report and analysis.
Key Takeaways:
Environmental deregulation on the federal level was the biggest trend that emerged from the United States in Q1 of 2026.
At the start of the year, two significant reporting policies from the European Union took effect, and businesses recently received some relief thanks to an omnibus simplification package that was approved.
China has approved a landmark environmental code that brings together more than 10 existing laws, targets pollution, and formalizes its carbon market.
Song of the Week:
The post Supply Chain and Logistics News (March 30th- April 2nd 2026) appeared first on Logistics Viewpoints.
The Supply Chain Cost Stack: Where Margin Is Actually Engineered
Nike and the Converse Question: Operate or Orchestrate the Asset
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