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Building an Industrial Blueprint for Business Agility

Published

5 mois ago

20 mai 2026

This is the third in a blog series of four that reviews discussion that occurred during ARC Advisory Group’s 2026 Industry Leadership Forum. Specifically, it details a keynote conversation held with senior executives from Rolls-Royce, BTX Precision, and MxD. Read the full four-part series here: Connected Manufacturing Networks and the New Supply Chain – Logistics Viewpoints

Pillar 2: The Demand Architect

In my previous post, I explored The Market Signal and the shift to real-time, non-linear markets, the first pillar of the modern industrial economy. Today, I’ll dig into the Demand Architect, the orchestrator responsible for fulfilling the value outlined by the market signal.

For decades, the standard operating procedure for industrial OEMs, particularly in discrete markets, was some form of issuing a purchase order, waiting for the parts to arrive, and swinging the penalty stick at the supply chain ecosystem if the order didn’t unfold as planned. That linear, somewhat adversarial approach worked adequately. Today, that transactional model eventually guarantees failure, and those that continue to bake it into their operational DNA are already falling behind.

In today’s increasingly connected business environment, market signals increasingly necessitate decisions that are proactive, accurate, and fast. Passive reaction doesn’t work and will invite disruption that will lead to obsolescence.

During the Wednesday keynote in February at the 30th annual ARC Industry Leadership Forum, I introduced the Demand Architect, exemplified by Greg Davidson, Global Commodity Executive at Rolls-Royce. The Demand Architect is a proactive orchestrator, designing and aligning an incredibly complex ecosystem to respond to the compressed market signals. Greg provided details and examples as to why this second pillar of the new, growing industrial reality is certainly the most visibly disruptive, but also the most necessary.

Moving From Dictation to Orchestration

True industrial innovation and transformation require structural agility and a fundamental shift in leadership and, subsequently, decision making across the organization. Davidson discussed his company’s transition into the Demand Architect role during our panel. On the surface, opening up data and collaborating deeply with external partners seems counterintuitive to lowering risk. However, he noted that following the post-COVID explosion in aerospace demand, Rolls-Royce realized that their traditional “directed buy” model—where the tier-one supplier was solely responsible for managing everything below them—was breaking down under market stress.

While I’m not suggesting leadership necessarily needs to change, that just happened to be the catalyst for Rolls-Royce. The shift began with a new CEO, who came from another industry and so was able to view the aerospace supply chain through a completely different lens. He asked his leadership team a pragmatic question: “What do you guys need to be successful?” It turns out that the answer fell, in part, outside the walls of the organization. Rolls-Royce needed to more aggressively support and collaborate with its supply chain ecosystem.

To be a true Demand Architect, which they saw as mission critical, they had to treat their external partners as extensions of their own enterprise. As an example, they shifted personnel into their raw material teams, expanding it to more than 400 people globally, to actively manage bottlenecks down to the second and third tiers.

Reorganizing for Responsiveness: The “Central Tower” and the “SOS Team”

They also understood that internal changes were required to develop and facilitate a more responsive ecosystem. Rolls-Royce tackled this by creating a “central warehouse” or “central tower” team. Rather than staffing this group only with traditional supply chain experts, they embedded IT and data analytics personnel directly into the team. Their sole job is to build data-driven information capabilities that help segment out market noise, better project demand, and provide operational transparency across the value chain.

Even more radically, they developed an internal “SOS team.” This is a rapid-response group of cross-functional experts, ranging from engineering to demand profiling, with the autonomy to parachute in to help a struggling supplier. Whether a supplier is dealing with casting porosity or struggling to secure rare earth minerals, the SOS team deploys to solve these bottleneck problems that introduce latency in meeting market signals.

Rolls-Royce also put effort into removing any commercial friction related to these interventions. When a severe technical issue arises, they mandate peer-to-peer engineering discussions. An engineer from Rolls-Royce will sit down with the same from the supplier to collaborate on how to fix the problem. Commercial buyers are explicitly banned from the conversation so that any improper vested interest doesn’t influence the outcome. The focus is entirely on the art of the possible and keeping the supply chain moving, not on imposing negative consequences, such as financial penalties.

Tearing Down Walls Without Exposing IP

Achieving this level of architectural agility isn’t easy. It forces incredibly risk-averse companies to overcome the pervasive fear of exposing mission-critical intellectual property. By starting with the market signal, Rolls-Royce was able to define the necessary outcome. In doing so, the value of the benefit was clearly well beyond the risk, which actually turned out to be perceived rather than real once the improvements were put in place.

As I discussed with a few of my ARC colleagues later in the Forum, the solution isn’t to recklessly open the floodgates of data, but to create targeted, secure pathways. Rolls-Royce, for instance, is pushing the envelope by creating new collaborative teams that actively work with and extend into their manufacturing supply chain’s processes. They are fundamentally changing how humans across different companies work together to meet a shared goal.

This is where technology actually earns its keep. The Demand Architect relies on an underlying technical foundation—specifically, Industrial Data Fabrics acting as silo breakers, and Agentic AI to facilitate secure, autonomous communication. By using AI as a targeted toolbox rather than a blanket solution, the Demand Architect manages the massive coordination complexity of this new ecosystem. Technology handles the data meshing and risk mitigation, freeing up the human workforce to do what they do best: focus on delivering production value.

Moving Past Technology Use for the Sake of Technology

Orchestrating a massive, multi-tiered ecosystem requires contextualized data sharing and advanced technological tools. Inevitably, this brings us to the elephant in every room today in artificial intelligence. As I have noted recently, AI is currently the shiniest of shiny objects. The temptation is to throw AI at every process, which inevitably leads to stranded coding, massive computing costs, and counterproductive software bloat. Or as Berardino Baratta, CEO of MxD, perfectly encapsulated, “You don’t need an AI strategy. You need a business strategy that fixes your problems, and AI might be the tool to fix it.”

Innovation leaders approach AI as a powerful toolbox, not a standalone outcome. They use AI to analyze shared design models for manufacturability, to secure data fabrics, and to automate agent-to-agent communication. Furthermore, they strive for VOI (Value on Investment) rather than just traditional ROI, understanding that the strategic value of real-time ecosystem agility exponentially outweighs short-term, isolated efficiency gains.

Finally, successful Demand Architects understand that AI does not replace human ingenuity. Rather, it augments it. If an enterprise attempts to replace human operational knowledge with the artificial variety too quickly, it will cripple its ability to innovate. An effective Demand Architect also recognizes that an enterprise is only as agile as its weakest supplier, and true competitive advantage is built by actively engineering the success of the entire ecosystem.

In our final blog in the series, I’ll outline the third pillar in the new fabric of demand, the agile partner. The blog will outline how companies like BTX Precision have shown market leadership by structurally reorganizing their ecosystem to continuously align with complex market signals in a hyperconnected business world.

The post Building an Industrial Blueprint for Business Agility 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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The post The New Economics of Logistics Visibility appeared first on Logistics Viewpoints.

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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.

The post o9 Solutions Uses a Knowledge Graph to Connect Supply Chain Decisions appeared first on Logistics Viewpoints.

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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.