Octave’s Mission: Building Systems That Don’t Break

At Hexagon’s recent global leadership event Hexagon LIVE, Mattias Stenberg, who was appointed in October 2024 to lead Octave, a strategic spinout from Hexagon, delivered a keynote that was both grounded and far-reaching. His talk offered a clear-eyed assessment of the state of critical infrastructure today, and what needs to change if we want our most essential systems to function reliably in the future.

Stenberg, who has been president of Hexagon’s Asset Lifecycle intelligence division since 2017, wasn’t there to announce a product line or unveil another dashboard. His message was more fundamental: we’ve inherited systems built to break. Octave’s mission is to build ones that don’t.

A New Kind of Intelligence for the Real World

Octave is not just another brand. It’s designed as an intelligence layer for the infrastructure that keeps society running. Think power grids, manufacturing systems, highways, water networks, emergency operations centers. The kind of systems where failure isn’t just inconvenient, it’s dangerous, costly, or even catastrophic.

At the core of Octave’s approach is a simple shift in mindset: stop reacting, start anticipating. Octave doesn’t aim to flood teams with more data or more disconnected tools. It aims to turn existing data into foresight, to embed real-time, contextual intelligence into the systems we rely on every day.

The name “Octave” itself is no accident. Inspired by music, it reflects a higher level of coordination and elevation, not just making noise, but orchestrating decisions. In practice, that means integrating AI directly into physical systems so they can detect issues, adapt, and respond long before human operators are even aware a problem is coming.

The Problem: Everything Breaks

Stenberg talked directly – “Things break,” he said, invoking the second law of thermodynamics with a hint of humor.

He pointed out examples we all recognize from the news:

• The Francis Scott Key Bridge collapse, a structural failure that could have been prevented with early detection.
• The Deepwater Horizon disaster, caused by system blind spots and poor integration between safety layers.

He pointed out that in each case, the data existed. The systems failed because they weren’t connected in the right way and lacked the intelligence to interpret early signals.

Octave’s Vision: Failure Is Not Inevitable

Too many organizations accept breakdowns as part of doing business. Delays, downtime, asset failure, they’ve been normalized. Octave’s entire premise is to challenge that mindset.

If systems can sense changes in their environment, process those signals intelligently, and act on them in time, then many failures can be avoided. But that level of performance requires embedded, context-aware intelligence, not generic AI models running in a separate system. Octave’s agents aren’t passive tools waiting for prompts. They’re active participants in system behavior.

Already, Hexagon has started deploying this approach across sectors, from manufacturing and logistics to public safety and energy. The early returns, he stated, are promising: less downtime, better coordination, and more resilient performance.

The Digital Transformation Disconnect

To underline the need for a new approach, Stenberg shared data from a recent C-suite survey conducted by Hexagon’s Asset Lifecycle Intelligence division,:

• Only 1 in 5 companies say they are realizing the full value of their digital transformation investments.
• 76% report using more tools and dashboards than ever, but feel less aligned and less in control.
• One executive summed it up: “More dashboards. More complexity. Less actual visibility.”

This reflects a deeper truth. Digital transformation alone doesn’t guarantee better performance. In fact, if not managed carefully, it can add noise, create silos, and obscure decision-making.

What Octave is offering is a reset: cut the clutter, connect what matters, and turn data into decisions that move the needle.

AI: Less Hype, More Usefulness

Stenberg then discussed the current AI hype cycle.

Yes, AI holds enormous potential, but most AI systems today are untrained, detached from operations, and built in isolation. They’re promising demos in a lab, not solutions in the field.

Octave takes the opposite path. Its AI agents are trained for specific environments, tied into operational systems, and continuously learning. These aren’t showpieces, they’re in the loop, helping machines and humans respond to signals in real time.

The Mirror World: Not Just a Twin, But a Partner

The term “digital twin” has been used in nearly every industry over the past few years. But as Stenberg pointed out, many of these twins are little more than static visualizations, nice to look at, but lacking predictive value.

Octave, Stenberg said, will be redefining the concept. In its vision, the Mirror World is a living, learning model of a physical system, constantly updated, deeply embedded, and able to act. If your digital twin can’t help you see trouble coming, it’s just a digital museum.

The goal is to spot patterns early, detect small signals before they become big problems, and support decisions that prevent, not just mitigate, failure.

Voices from the Field: What Must Not Break

Stenberg’s keynote also featured a panel of customers speaking directly about the pressures they face. Each one brought a unique perspective, but all centered on resilience and visibility.

Donald Lhoest (Carmuese): Global logistics operations require consistent, trustworthy intelligence. When teams are remote and distributed, “data needs to empower, not isolate, them.”

Colonel Mark Shelley (Lee County Sheriff’s Office): “Public safety depends on integrated, real-time intelligence. There’s no margin for error.”

Wade McNabb (Lixil): Factory downtime is a major risk. But human error is just as dangerous. “We need better training systems, and better insight into how our customers actually use our products.”

Joe Bonnet (Worley): “We run 10,000 projects at once. Complexity itself is the problem. If we can eliminate failure points, we can focus on innovation.”

Proof in Practice: The Öresund Bridge

To bring the message home, Stenberg closed with a case study:

The Öresund Bridge, connecting Sweden and Denmark, is equipped with a real-time digital twin. The system detected subtle vibration patterns, signals no human would have caught on their own.

Engineers investigated and discovered microcracks forming. Intervening early prevented what could have been a major infrastructure failure. That’s not theory. That’s the Mirror World in action.

Final Message: Time to Rethink What We Accept

Stenberg’s final words were a challenge to the industry.

“We’ve inherited systems that were built to break. Octave’s job is to build systems that adapt, and don’t fail silently.”

It is not about layering more dashboards or adding complexity. It’s about designing smarter from the start, systems that can think, respond, and evolve.

The post Stop Reacting, Start Anticipating: Hexagon’s Octave Vision for a Smarter and More Resilient Industrial and Commercial Infrastructure appeared first on Logistics Viewpoints.

For years, supply chain professionals have talked about visibility, resilience, and efficiency. The tools we have used, ERP systems, spreadsheets, and siloed databases, have served us well, however as complexity increases, and the margin for error narrows, there has been a growing recognition that patchwork systems are no longer enough.

What is needed is a practical, scalable way to unify supply chain data across systems, make it useful in real time, and apply intelligence, whether from algorithms, machine learning models, or a trained human eye, to act on it quickly. That is exactly the direction taken by InterSystems and their customers, as detailed at InterSystems READY 2025 in Orlando, Florida.

Data Fabric Studio: One Place to Start

At the center of the discussions was the InterSystems Data Fabric Studio, a cloud-based system designed to integrate and organize data from multiple sources. Not just for IT departments or data scientists, but also for the people who manage daily operations, procurement leads, planners, and inventory analysts.

This product connects directly to systems like Snowflake, Kafka, AWS S3, and relational databases. It allows users to build and automate workflows (called “recipes”) that clean, reconcile, and move data into consistent formats, without having to code from scratch.

In short, it helps turn fragmented operational data into something trustworthy, structured, and ready for use across departments.

Use Case: Supplier Data Integration Across ERPs

One session focused on a familiar problem: integrating supplier data spread across two disconnected ERP systems. Each system used different IDs for the same suppliers, different formats for purchase orders, and different rules for reconciliation.

Using Data Fabric Studio, the team:

• Mapped and validated key identifiers (like DUNS numbers) across systems.
• Flagged inconsistencies in supplier names and standardized the records.
• Created lookup tables and transformation rules to automate future loads.
• Set a schedule to refresh this data daily, no manual uploads are needed.

The takeaway: fewer errors, faster onboarding, and one consistent view of supplier performance.

Forecasting, Not Just Reporting

Several sessions went far beyond integration, since once data is unified, it becomes possible to do more with it, like improve forecasts or detect early signs of trouble.

One method shown was to create snapshots of data tables at regular intervals, such as open purchase orders at the start of each week or inventory by location at shift change. These snapshots could then feed planning tools without requiring repeated rework or new queries every time someone asks for an update.

It is not classical predictive AI, but it is the kind of practical structure that supports accurate forecasting and decision-making.

AI Integration

Many AI projects fail not because of the models themselves, but because the data going into them is disorganized or outdated. InterSystems’ position, with which ARC strongly agrees is that data must be AI-ready, structured, validated, and governed, before AI can be reliably applied.

For those who are ready, Data Fabric Studio includes native support for vector search and retrieval-augmented generation (RAG). This means it can:

• Embed semantic search into procurement or customer service workflows.
• Feed large language models with accurate, up-to-date information drawn from verified data.
• Support natural language interfaces, including assistants that generate SQL or explain trends.

One example came from Agimero, a European firm that used vector search to streamline parts procurement. They built a semantic layer into their sourcing tool, which helped reduce turnaround time and freed up staff. Time to deploy? Less than a week!

Lessons from Healthcare That Apply Here

A keynote from the AI for Healthcare track may seem unrelated at first, but the core lesson was broadly applicable: data doesn’t have to be clinical to be useful in diagnosis. In one case, the team used shopping data to detect early signs of ovarian cancer based on changes in food purchases.

Now let’s translate that into supply chain language.

What if sudden shifts in supplier invoicing patterns indicated financial stress? What if internal communications flagged increasing lead times before they hit the dashboard? The tools now exist to explore those questions, not just log them.

The point is it is time to examine where such signals might live in your own systems.

A Modular Approach That Does Not Lock You In

Another strength of the Data Fabric Studio is its modular design. You can start with basic data ingestion and cleaning, then layer on adaptive analytics, natural language assistants, or domain-specific modules (e.g., for supply chain, finance, or healthcare) when and if they make sense.

Unlike some vendor ecosystems, this one doesn’t insist you move everything into a new system. It works alongside existing data warehouses, ERP tools, and planning platforms. That flexibility matters, especially for organizations that cannot afford multi-year migrations.

Vector Search and RAG: Where It Fits

The integrated vector search capabilities shown during the sessions were grounded, not speculative. One demonstration showed how a company used it to improve search across 400 million records of biological data. The same tools were used in supply chain use cases, surfacing similar suppliers, matching part numbers across catalogues, or enabling text-based queries across historical documents.

These systems don’t replace human judgment, but they make pattern recognition faster, and reduce the time spent digging through dashboards and reports to get to the relevant piece.

Scalability Is not Optional

For companies working on a global scale, performance is key. Sessions with Epic (the healthcare software company behind MyChart) showed how InterSystems IRIS, the underlying engine behind Data Fabric Studio, supports hundreds of millions of real-time transactions.

Why mention this in a supply chain context? Because once data becomes foundational to operations, slow queries and manual workarounds no longer are enough. The infrastructure must keep pace.

InterSystems have built their offerings with that in mind, whether for healthcare, finance, or logistics.

What stood out across all the sessions was not hype, there was a clear theme:

• Organize your data first.
• Reconcile it across systems.
• Use automation to reduce repeat work.
• Add intelligence gradually, where it supports decisions.
• Prioritize infrastructure that can scale.

If you have worked in supply chain for any length of time, that list is no surprise, however seeing those steps pulled together in one single system, accessible to both developers and business users, is important and unique.

Digital Transformation is about doing what works, better, faster, and with less friction, and that is exactly what was seen at InterSystems READY 2025.

The post InterSystems READY 2025 – Modern Supply Chains, Practical Data Strategy, and Tools That Work appeared first on Logistics Viewpoints.

Supply chains do not just move products, they move promises. Promises of origin, safety, compliance, sustainability. And in 2025, keeping those promises has never been harder. Counterfeit goods still fill markets. Paper trails remain vulnerable. Disparate systems fail to communicate. Trust, the currency of global trade, is stretched thin.

Blockchain, despite the early hype and hesitation, is proving to be the foundational architecture that this trust-deficient world demands.

The Trust Deficit at the Core

A modern supply chain spans continents and cultures. Multitudes of suppliers, each using different data formats and systems, must somehow coordinate seamlessly. Yet in too many cases, a manufacturer still cannot verify where their materials originated. A retailer depends on vulnerable documentation. Regulators face opaque claims around sustainability, emissions, and labor practices.

This is not a technology problem. It is a data trust problem.

Blockchain addresses that problem by design. Instead of relying on reconciliations between siloed systems, it offers a single, tamper-resistant ledger. Every authorized party, supplier, auditor, regulator, buyer, can read from and write to the same shared source of truth.

What Blockchain Really Does

Strip away the buzzwords, and blockchain is a decentralized, immutable database. Each transaction is time-stamped, cryptographically secured, and permanently linked to those before it. Once recorded, it cannot be altered without network-wide consensus.

This delivers three things that every supply chain needs:

• Provenance: A verifiable chain of custody from raw material to finished good.
• Smart contracts: Automated enforcement of terms, from payment triggers to regulatory compliance.
• Auditability: A permanent, tamper-proof log accessible to auditors, customers, and internal teams alike.

Real-World Impact

Blockchain is not an academic exercise, it’s already in use by industry leaders:

• Renault Group moved its entire supply chain documentation process onto blockchain. The result? Real-time compliance and document sharing across its automotive ecosystem.
• The Home Depot adopted blockchain to enhance supplier visibility, reduce disputes, and shorten issue resolution timelines.
• Valencia Port Foundation partnered with IBM to integrate blockchain into its port logistics, enabling secure, trusted data exchange among shippers, customs, and terminal operators.
• A pharmaceutical pilot involving KPMG, Merck, IBM, and Walmart demonstrated how blockchain can reduce drug traceability from 16 weeks to just 2 seconds, a breakthrough in counterfeit prevention.
• Vertrax Blockchain, operating in the energy sector, enabled real-time logistics traceability during high-stress demand spikes brought on by severe weather events.
• IBM Food Trust continues expanding, allowing retailers to trace fresh produce from farm to shelf, a vital tool in managing food safety, recalls, and expiry concerns.

A Cybersecurity Bonus

The decentralized nature of blockchain makes it inherently resilient. There’s no single point of failure. Every transaction is encrypted and linked, rendering unauthorized changes virtually impossible.

When integrated with IoT and digital twins, blockchain becomes the backbone of a secure “digital thread”, maintaining integrity from the origin of raw materials to the final point of sale. Predictive analytics layered on top allow for real-time anomaly detection, flagging issues before they become crises.

The Integration Challenge

Blockchain is not a silver bullet. It is not plug-and-play. It requires:

• Shared governance across ecosystems.
• Standardized data models between trading partners.
• Tight integration with core systems like ERP, PLM, and SCM.
• Significant change management, especially in training and stakeholder buy-in.

Technology alone will not deliver ROI. Blockchain demands a shift in how supply chains view data: not as proprietary control points, but as shared assets that drive collective value.

Why It Matters More Than Ever

Three forces make blockchain’s role non-negotiable in 2025:

1. ESG and Regulatory Demands: Governments now require traceability, not just for carbon, but for labor practices, material sourcing, and ethical compliance.
2. Consumer Expectations: The modern buyer wants transparency. QR codes should answer where, how, and by whom a product was made.
3. Resilience in a Disrupted World: Geopolitical risks, climate volatility, and cyber threats make real-time, trustworthy data a survival requirement.

Final Thoughts

Blockchain is no longer a science experiment, it is an operational pillar. It transforms integrity from a virtue into a verifiable asset. For enterprises navigating uncertainty, it offers more than security. It offers clarity.

In a world where trust must be earned with every transaction, blockchain does not just support the supply chain. It secures it.

The post Blockchain for Transparent and Secure Supply Chains, 2025 Update appeared first on Logistics Viewpoints.