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Mastering Disruption: A Smarter, More Connected Approach

Published

2 ans ago

27 mars 2025

Mastering Disruption: A Smarter, More Connected Approach

Five years ago, we all thought the COVID-19 pandemic resulted in the most disrupted supply chain landscape we would ever see. We were wrong. Since then, supply chain disruptions and volatility have only increased.

Three months into 2025, we have seen a barrage of on-again, off-again tariffs that have supply chain and logistics teams reeling, as they must rethink everything from next week’s shipping route to their foundational network models. From wildfires and flooding to tornadoes and hurricanes, climate change contributes to more frequent and powerful disasters. The Ukraine-Russia conflict is ongoing. Tensions flare in the Middle East without warning.

A disruption at any point in the global logistics network — including the average of 12 touch points from shipment packaging to final delivery — can prove disastrous for profits, service levels, customer loyalty, and other key metrics. With the global e-commerce market predicted to reach $8.1 trillion next year, omnichannel revenues may be increasing — but so are the chances that something, somewhere, will go wrong on the journey of getting orders to the customer.

Today the question is not just “When is the next disruption coming?” but also “How well can we proactively avoid the damage it may cause?”

Most supply chain and logistics teams have recognized that the only way to combat today’s incredible level of uncertainty is by adopting and applying digital tools. The pace and scope of supply chain disruption are beyond human cognition, manual analysis, and consumer-grade spreadsheet tools. With its ability to monitor conditions across the supply chain — at every node and touch point — digitalization provides the only practical solution.

Kudos to the supply chain and logistics teams that have already adopted transportation management systems (TMS), warehouse management systems (WMS), and other digital solutions. They can ingest large volumes of functional data and leverage advanced intelligence to recognize broad trends and specific disruptive events. They are applying predictive analytics and data science to choose an optimal response quickly, driven by facts and pre-defined business outcomes.

It is not surprising that the TMS market will nearly double in size between 2024 and 2029, increasing from $11.75 billion to $23.07 billion. Similarly, the WMS market is expected to grow from $3.9 billion in 2023 to $13.3 billion by 2030, more than tripling in size.

As the Stakes Get Higher, Technology Is Growing in Reach and Capability

While having a TMS or a WMS is a great place to start, many supply chain and logistics teams may not realize that they are only scratching the surface of modern supply chain digitalization. The logistics domain’s and industry’s leading software providers recognize that supply chain disruptions and volatility are growing — and they have responded with some powerful innovations.

Supply chain and logistics teams owe it to themselves to learn about the new generation of advanced digital solutions— and associated best practices— that are changing the nature of logistics networks today. Generally, next-gen innovations fall into a few main categories, discussed below.

Enabling an immediate and synchronized response

Ideally, supply chain and logistics teams would respond to every disruptive event immediately — making the best, most informed decision and then orchestrating it across thousands of miles of supply chain. That may sound impossible, but new technology places this capability within the reach of every organization. There are two components involved here: making the right decision and executing it in a synchronized manner.

Artificial intelligence (AI) is not just a buzzword; it has become a critical competency for supply chain and logistics teams today. Its value is recognized by shippers as they choose logistics partners. In a recent study, almost three-quarters (74%) of shippers reported they would switch to 3PL providers based on their AI capabilities.

Why? Because only AI is capable of ingesting real-time data from across functions, facilities, fleets, trading partners and the outside world — then using data science and pattern recognition — see disruptions at the earliest moment. AI is also making it possible to define a response that balances multiple outcomes, such as cost, service, profitability, and sustainability, applying pre-defined rules and guardrails. It is no wonder that the AI in the supply chain market will grow more than 10x between 2024 and 2030, from $5 billion to $51 billion.

AI is only the beginning of generating the most effective response to disruptions. Leading supply chain and logistics teams can drive an automated, collaborative response to exceptions by uniting interoperable solutions — like WMS, TMS, planning, order management, and yard management systems — on a shared platform. Wherever the disruption occurs in the end-to-end supply chain, a platform approach and interoperable solutions guarantee shared awareness of the event and a broad, cross-functional response that intelligently balances all outcomes. The responses are more effective, and response time is much quicker, with fewer buffer stocks.

Just as a single solution, like a TMS, can autonomously reroute a shipment when a port closes, the end-to-end supply chain can act immediately, in synchronization, with little to no human intervention. More broadly, AI can be deployed across functions to shift inventory, switch transportation modes, find new carriers, communicate across functions and regions with customers and partners, and otherwise deliver a smart, collaborative response. That is the beauty of a platform enabled by AI.

Adopting a platform-based approach can be a game-changer for today’s embattled supply chain and logistics teams — creating a seamless and effective way to recognize a disruption and pull a single execution lever in response.

Redefining the concept of a logistics network

The capabilities of AI in recognizing disruptions and changes and fueling synchronized planning and execution are limitless. For most supply chain and logistics teams, their execution options are not limitless. Teams are constrained by their physical resources, like trucks, inventory, and labor capacities, as they seek to resolve a disruption. They are also limited by their supplier, carrier, and trading partner networks.

One of the most exciting innovations happening in logistics today is eliminating these constraints via digitalization of the supply chain ecosystem. Real-time connectivity empowers the existing logistics network and opens the door to limitless opportunities for collaboration and partnership beyond the existing supply chain footprint.

By partnering with the right solutions provider, supply chain and logistics teams can connect with as many as 150,000 trading partners that can instantly and seamlessly extend the logistics network on demand. Whether supply chain and logistics teams are looking for new sources of inventory, transportation or warehousing, a full-service logistics software partner can seamlessly connect them with the right partners.

Even as the logistics network expands, digitalization guarantees all collaborators share the same data and awareness. They have real-time visibility into inventory levels, movements and purchase orders across all trading partners in the multi-tier network — from raw materials to warehouse to retail shelf or consumer doorstep.

The shift from a traditional, linear, constrained supply chain to a dynamic, interactive network has emerged as one of the smartest and most effective ways of managing logistics disruptions. After all, who does not want more options and greater agility when the unexpected happens?

Executing flawlessly at the task level

While the first two innovations described here focus on optimized end-to-end execution, enabled by AI and digitalization, today’s next-gen technology is changing how users complete every task and handle every item. In a recent interview published by Logistics Viewpoints, Blue Yonder CEO Duncan Angove highlighted the groundbreaking developments in agentic AI that are transforming the supply chain at a granular level.

The power of agentic AI lies in creating a new digital workforce that interacts directly with human associates. A team of interactive, AI-enabled optimization engines, or agents, are trained in specific logistics tasks like order prioritization, warehouse picking or load-building.

Supply chain and logistics teams can complement their human workforce with these specialized agents, each complete with their numeric algorithms, to accelerate and optimize key tasks. Human workers at the warehouse, for example, are guided by these AI agents, or co-pilots, as they complete their daily work via a user-friendly interface. Text and voice interactions are

possible, and these agents generate summaries and reports that allow associates to see both macro and micro-level performance results. Not only can agentic AI reduce warehouse labor costs by 25% and improve productivity by 15%, but it also increases employee satisfaction and retention.

Agentic AI is an easily learned and accessible way for many companies to derive quick returns from next-gen technologies. Blue Yonder has seen 5x growth in agentic AI applications year-over-year, and this emerging technology area is just getting started.

It is Clear: Supply Chains Must Exert Greater Control Over Disruptions

Industry statistics demonstrate clearly that the world’s supply chain and logistics teams are embracing the power of advanced technology and digitalization. As supply chain disruptions increase in frequency and scale, software providers are doing their part by investing in even more impactful technology innovations every day.

That is why supply chain and logistics teams need to see technology adoption not as a one-time event but as an ongoing journey. Companies that continuously explore and apply the newest innovations, like AI agents, will realize a significant edge over competitors who still rely on older technologies and highly manual work processes.

Looking back at the COVID-19 pandemic, who could have predicted that the world’s supply chains would only become more disrupted and challenged? Fortunately, from tariffs to extreme weather, today’s advanced technology allows supply chain and logistics teams to be far better prepared for the future — no matter what that future looks like.

About the Author

Terence Leung is Global Senior Director of Solution and Industry Marketing at Blue Yonder. With a keen interest in AI and digitalization and the benefits they generate, Terence is passionate about Blue Yonder’s industry-leading supply chain platform, which spans warehouse management, warehouse execution, yard management, transportation management, planning, and commerce solutions. He works closely with Blue Yonder customers to understand their challenges and requirements, helping them adopt best practices in their digital journeys.

Prior to joining Blue Yonder, Terence was the leader in product marketing and value engineering at One Network. He held previous leadership positions in industry management at Savi Technology and solution management and management consulting at i2 and Deloitte Consulting, respectively. Terence earned an MBA from the University of Texas, Austin, and an Electrical Engineering degree from MIT.

The post Mastering Disruption: A Smarter, More Connected Approach appeared first on Logistics Viewpoints.

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The New Economics of Logistics Visibility

Published

16 heures ago

5 octobre 2026

Knowing that a shipment will arrive six hours late is information. Knowing it early enough to reschedule labor, protect a customer commitment, avoid detention, or change an inventory decision is economic value.

That distinction is becoming central to the logistics visibility market. The earlier argument that exceptions are becoming the real unit of work explains why visibility economics depend less on event volume than on whether the organization can convert important events into timely resolution.

The first era of visibility was largely about answering a basic question: Where is my shipment? The next era is about a harder question: What should I do because its state has changed?

Visibility Is Not the Outcome

Location and status data can be valuable, but they are intermediate products. A business does not earn a return because a dot moved across a map more accurately. The return appears when information changes an operational decision. A useful way to think about visibility is as a chain: signal -> interpretation -> decision -> intervention -> economic outcome. If any link is missing, much of the potential value disappears.

A Signal Has to Arrive Inside the Decision Window

Timing matters.

A delay discovered after the customer has already missed production is history. The same delay identified early enough to expedite an alternate shipment may be actionable. An ETA update received after warehouse labor has reported for a shift may have less value than the same update received while the schedule can still be changed.

This means visibility quality is not only about accuracy. It is about whether the signal arrives with enough lead time to support an intervention.

Not Every Exception Deserves Attention

As visibility improves, organizations often discover a new problem: too many exceptions. A network with thousands of shipments will always contain delays, deviations, missed scans, changing ETAs, and incomplete data. If every deviation creates an alert, planners become the bottleneck. The more important capability is prioritization.

Which late shipment threatens a high-value order? Which delay creates a stockout? Which container risks demurrage? Which arrival change will disrupt a dock schedule? Which event is likely to self-correct without intervention?

Visibility becomes intelligence when the system can distinguish operational consequence from mere deviation.

ETA Is a Decision Input

Estimated time of arrival is a good example of how the economics are changing. ETA was once primarily a customer-service or tracking metric. Increasingly it can influence warehouse scheduling, yard planning, labor, inventory, customer promises, and downstream transportation. That makes ETA a shared operating variable.

The value increases when the prediction is connected to the systems that can respond. A changing ETA that remains trapped in a visibility dashboard creates less value than one that can trigger a workflow or decision elsewhere.

Dwell, Detention, and Demurrage Make the Economics Visible

Some visibility use cases have direct financial consequences. Better awareness of arrival, dwell, free-time windows, and container status can help organizations manage detention and demurrage exposure. Yard visibility can reduce unnecessary trailer search and moves. Earlier exception detection can protect delivery appointments and reduce costly service recovery. These cases make an important point: visibility value is often realized outside the visibility platform itself.

More Visibility Can Increase Work

This is the uncomfortable side of digital transparency. If a company exposes ten times as many events but does not improve prioritization or workflow, it may create ten times as many things for people to inspect. The result can be an expensive monitoring layer sitting on top of the same manual decision process.

That is why visibility and autonomous exception management are converging. The system must increasingly help decide which events require action, assemble context, recommend a response, and automate routine resolution where appropriate.

Measure Intervention, Not Just Coverage

Visibility programs are often measured by tracking coverage, data completeness, ETA accuracy, or number of connected carriers. Those are necessary operating metrics, but they do not fully describe business value.

Organizations should also ask: How many material exceptions were identified early enough to act? How quickly were they resolved? How often did intervention protect service or avoid cost? How many alerts required no useful action? How much planner time was consumed per exception?

Those measures connect visibility to economics.

The Market Is Moving Toward Action

This shift has strategic implications for technology providers. Pure visibility is becoming less differentiated as location and event data become more widely available. The higher-value layer is interpretation and action: understanding what an event means to a specific operation and helping execute the appropriate response.

That pushes visibility platforms toward orchestration, workflow, decision intelligence, and AI. It also pushes TMS, WMS, and other execution systems toward richer external event awareness.

The Bottleneck Moves

For years, logistics organizations complained that they could not make better decisions because they could not see what was happening. Increasingly, they can see more.

The bottleneck is moving.

When a network can identify exceptions continuously, the constraint becomes the speed and quality with which the organization can interpret and resolve them. That is precisely the environment in which AI agents become interesting—not because logistics needs another conversational interface, but because it needs more capacity to do operational work.

Related Logistics Viewpoints research

The New Architecture of Logistics
Systems Engineering in Logistics
2026 Autonomous Exception Management Market Map
The Economics of Decision Latency
Previous in this series: Transportation Is Becoming Computational

Request The New Architecture of Logistics Client Edition

If your organization is assessing connected execution, orchestration, AI, observability, decision velocity, or selective autonomy, I would be glad to provide the complete client edition and discuss the implications for your logistics operating model and technology architecture.

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o9 Solutions Uses a Knowledge Graph to Connect Supply Chain Decisions

Published

17 heures ago

5 octobre 2026

Supply chain decision-making is difficult partly because the relevant information is distributed across products, locations, suppliers, orders, capacities, policies, and external events. A system may have access to all of those records and still struggle to understand the relationships among them quickly enough to support a consequential decision.

o9 Solutions addresses that problem through its Digital Brain architecture, including an enterprise knowledge graph and in-memory modeling designed to connect demand, supply, inventory, planning, and operating context. That semantic layer is important because it gives analytics and AI a structured representation of how supply chain entities relate to one another rather than treating the environment as a collection of independent tables and documents.

The company combines this architecture with integrated planning, optimization, scenario modeling, machine learning, and human oversight. The strategic direction is toward a continuous decision environment where changes can be interpreted quickly, alternatives can be modeled, and recommendations can be traced back to the assumptions, events, and constraints that produced them.

As with any broad planning and intelligence platform, the value depends on implementation quality. Knowledge models need strong data governance, entity resolution, process ownership, and clear decision rights. A sophisticated model of the supply chain is useful only if the organization can keep it current and use it consistently in real operating workflows.

o9 Solutions appears in the Logistics Viewpoints Supply Chain Decision Intelligence MarketMap and Autonomous Exception Management MarketMap. The two MarketMaps highlight the relationship between integrated decision intelligence and the faster exception-response capabilities now developing around it.

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AI Will Not Transform the Supply Chain Until the Architecture Around It Catches Up

Published

20 heures ago

5 octobre 2026

Executive thesis. The model is becoming the least durable layer of the AI stack. Sustainable advantage will come from the operating architecture around AI: authoritative context, governed tools, permissions, observability, and connection to enterprise workflows.

The model is only one component

Supply chain AI discussions often begin with model capability: prediction accuracy, reasoning quality, computer vision performance, or the fluency of a generative system. Those capabilities matter, but operational value depends on everything around the model. The system still needs authoritative context, enterprise tools, permissions, workflow, observability, and a reliable path from recommendation to action.

Different AI patterns solve different problems

Prediction, optimization, generative AI, vision, and agents should not be treated as interchangeable technologies. Forecasting demand, selecting a route, extracting information from a document, interpreting an image, and executing a multi-step workflow require different evidence, control, and performance measures. A mature architecture starts with the decision or task and chooses the AI pattern that fits it.

Context is the operating fuel

An AI system can produce a plausible answer while using stale or incomplete operational context. In logistics, that can be dangerous because the truth may reside across orders, inventory, rates, carrier status, warehouse state, supplier records, and policy documents. Retrieval, master data, identity resolution, and system access therefore become part of the AI architecture, not secondary data-engineering concerns.

Tool access turns intelligence into consequence

The moment an AI system can create a shipment, change an order, contact a carrier, release inventory, or approve an exception, governance becomes an operational requirement. Tool permissions, financial limits, approval gates, idempotency, retries, and rollback are the mechanisms that separate an interesting demonstration from a dependable production workflow.

Measure workflow performance

A fluent response is not the right success metric for operational AI. Supply chain leaders should measure decision latency, manual context gathering, exception closure, override behavior, error recovery, tool failure, and the business outcome being improved. That measurement discipline also creates a rational basis for expanding autonomy as evidence accumulates.

The Logistics Viewpoints AI in Logistics: Use Cases, Architecture, and Implementation Guide separates the major AI patterns and connects them to data, tools, governance, workflow, observability, and ROI—the architecture required to move from model capability to operating value.

Executive implication

AI strategy should separate model selection from control architecture and measure value through workflow performance, decision quality, and operational outcomes.

Go deeper: provides the durable buyer, architecture, and implementation reference for this topic. AI & Advanced Analytics connects this analysis to the broader Logistics Viewpoints research architecture.